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Journal articles on the topic 'Crop disease estimation'
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Shahi, Tej Bahadur, Cheng-Yuan Xu, Arjun Neupane, and William Guo. "Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques." Remote Sensing 15, no. 9 (May 6, 2023): 2450. http://dx.doi.org/10.3390/rs15092450.
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Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection.
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Patil, Rutuja Rajendra, Sumit Kumar, Shwetambari Chiwhane, Ruchi Rani, and Sanjeev Kumar Pippal. "An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases." Agriculture 13, no. 1 (December 23, 2022): 47. http://dx.doi.org/10.3390/agriculture13010047.
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The pathogens such as fungi and bacteria can lead to rice diseases that can drastically impair crop production. Because the illness is difficult to control on a broad scale, crop field monitoring is one of the most effective methods of control. It allows for early detection of the disease and the implementation of preventative measures. Disease severity estimation based on digital picture analysis, where the pictures are obtained from the rice field using mobile devices, is one of the most effective control strategies. This paper offers a method for quantifying the severity of three rice crop diseases (brown spot, blast, and bacterial blight) that can determine the stage of plant disease. A total of 1200 images of rice illnesses and healthy images make up the input dataset. With the help of agricultural experts, the diseased zone was labeled according to the disease type using the Make Sense tool. More than 75% of the images in the dataset correspond to one disease label, healthy plants represent more than 15%, and multiple diseases represent 5% of the images labeled. This paper proposes a novel artificial intelligence rice grade model that uses an optimized faster-region-based convolutional neural network (FRCNN) approach to calculate the area of leaf instances and the infected regions. EfficientNet-B0 architecture was used as a backbone as the network shows the best accuracy (96.43%). The performance was compared with the CNN architectures: VGG16, ResNet101, and MobileNet. The model evaluation parameters used to measure the accuracy are positive predictive value, sensitivity, and intersection over union. This severity estimation method can be further deployed as a tool that allows farmers to obtain perfect predictions of the disease severity level based on lesions in the field conditions and produce crops more organically.
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ti, Shru, and Nidhi Seth. "Estimation of Fungus/Disease in Tomato Crop using K-Means Segmentation." International Journal of Computer Trends and Technology 11, no. 2 (May 25, 2014): 58–60. http://dx.doi.org/10.14445/22312803/ijctt-v11p112.
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Zhao, Hengqian, Chenghai Yang, Wei Guo, Lifu Zhang, and Dongyan Zhang. "Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization." Remote Sensing 12, no. 12 (June 15, 2020): 1930. http://dx.doi.org/10.3390/rs12121930.
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The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.
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Chen, Shuo, Kefei Zhang, Yindi Zhao, Yaqin Sun, Wei Ban, Yu Chen, Huifu Zhuang, Xuewei Zhang, Jinxiang Liu, and Tao Yang. "An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation." Agriculture 11, no. 5 (May 7, 2021): 420. http://dx.doi.org/10.3390/agriculture11050420.
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Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.
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Shahi, Tej Bahadur, Cheng-Yuan Xu, Arjun Neupane, Dayle Fresser, Dan O’Connor, Graeme Wright, and William Guo. "A cooperative scheme for late leaf spot estimation in peanut using UAV multispectral images." PLOS ONE 18, no. 3 (March 27, 2023): e0282486. http://dx.doi.org/10.1371/journal.pone.0282486.
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In Australia, peanuts are mainly grown in Queensland with tropical and subtropical climates. The most common foliar disease that poses a severe threat to quality peanut production is late leaf spot (LLS). Unmanned aerial vehicles (UAVs) have been widely investigated for various plant trait estimations. The existing works on UAV-based remote sensing have achieved promising results for crop disease estimation using a mean or a threshold value to represent the plot-level image data, but these methods might be insufficient to capture the distribution of pixels within a plot. This study proposes two new methods, namely measurement index (MI) and coefficient of variation (CV), for LLS disease estimation on peanuts. We first investigated the relationship between the UAV-based multispectral vegetation indices (VIs) and the LLS disease scores at the late growth stages of peanuts. We then compared the performances of the proposed MI and CV-based methods with the threshold and mean-based methods for LLS disease estimation. The results showed that the MI-based method achieved the highest coefficient of determination and the lowest error for five of the six chosen VIs whereas the CV-based method performed the best for simple ratio (SR) index among the four methods. By considering the strengths and weaknesses of each method, we finally proposed a cooperative scheme based on the MI, the CV and the mean-based methods for automatic disease estimation, demonstrated by applying this scheme to the LLS estimation in peanuts.
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S, Jeevanraja, Vishnu Vardhan Reddy A, Ashok Reddy S, and Hari Krishna Reddy V. "Deep Learning for Crop Yield Forcasting in Agriculture Using Multilayer Perceptron and Convolutional Neural Networks." Journal of Soft Computing Paradigm 6, no. 4 (January 2025): 401–11. https://doi.org/10.36548/jscp.2024.4.006.
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The primary challenge facing the agricultural sector, which is essential for ensuring global food security, is enhancing crop productivity while effectively addressing the challenges posed by plant diseases. Advanced technologies have the potential to completely transform agricultural methods, especially in the areas of computer vision and machine learning. This study uses meteorological as well as fruit and vegetables image datasets to create an integrated agricultural decision support system for crop yield estimation and disease prediction. By enabling early plant disease detection and precise crop yield estimates, the system seeks to improve precision agriculture techniques. To analyze and classify the images and predict the possibility of crop disease harming fruits and vegetables, a Convolutional Neural Network (CNN) deep learning model is used. The Multilayer Perceptron algorithm is used to train the model using a large dataset that contains historical meteorological data, allowing it to identify patterns and connections between environmental conditions. Finally, farmers receive an SMS notice with prediction specifics.
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Doggalli, Gangadhara, Santhoshinii E, Manojkumar H G, Mitali Srivastava, Ganesh H S, Amruta Barigal, Anithaa V, Arfa Ameen, and Ritama Kundu. "Drone Technology for Crop Disease Resistance: Innovations and Challenges." Journal of Scientific Research and Reports 30, no. 8 (July 23, 2024): 174–80. http://dx.doi.org/10.9734/jsrr/2024/v30i82237.
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Drones have been used for diverse application purposes in precision agriculture and new ways of using them are being explored. Many drone applications have been developed for different purposes such as pest detection, crop yield prediction, crop spraying, yield estimation, water stress detection, land mapping, identifying nutrient deficiency in plants, weed detection, livestock control, protection of agricultural products and soil analysis. Drones can create georeferenced maps that pinpoint the exact location of disease outbreaks within a field. These maps help farmers and agronomists monitor disease progression and plan targeted interventions. Drone operations are highly dependent on weather conditions. High winds, rain, and fog can hinder drone flights and affect the quality of images captured. Addressing technical limitations, regulatory and safety concerns, economic barriers, and data management issues will be crucial for the widespread adoption of drones in agriculture. By overcoming these challenges, drone technology can become a vital tool in sustainable and effective crop disease management.
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Pfender, W. F., and S. C. Alderman. "Regional Development of Orchardgrass Choke and Estimation of Seed Yield Loss." Plant Disease 90, no. 2 (February 2006): 240–44. http://dx.doi.org/10.1094/pd-90-0240.
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A survey for choke, caused by Epichloë typhina, in orchardgrass seed-production fields in Oregon was conducted annually from 1998 to 2003. In all, 99 fields were inspected, 57 in more than 1 year, to produce a set of 217 observations on disease incidence. There was a significant increase in disease incidence in 38% of the revisited fields, and a significant reduction of incidence in 3%. Yearly increases in disease incidence were as high as 29% in individual fields, but the average yearly increase from 1999 to 2003 was 5 to 8%. In 1998, 60% of all surveyed fields were infested with choke and, by 2003, 90% were infested. Average disease incidence in fields in their first year of production was <2%, and average disease incidence in older fields was approximately 10%, in 2003. Seed yield loss was equal to disease incidence (percentage of tillers diseased), and we found no significant yield compensation in diseased stands. An observed correlation of disease incidence with disease prevalence (proportion of sampled sites infested w ithin a field) may permit simple estimation of incidence and, thus, of potential economic loss in an affected field. We estimate regional loss to the 2004 orchardgrass seed crop due to choke o be approximately $0.8 million.
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Yang, Xiucong. "Identification and Monitoring of Crop Pests and Diseases Based on Remote Sensing Technology." Transactions on Environment, Energy and Earth Sciences 3 (November 26, 2024): 130–36. https://doi.org/10.62051/btdqj764.
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Identifying and monitoring crop pests and diseases are crucial to agricultural production, and they directly affect crop growth and the quality of agricultural products. This paper reviews crop pest and disease identification and monitoring techniques based on remote sensing imagery, emphasizing their advantages in providing timely and accurate information. The paper mainly summarizes the data sources for crop pest and disease detection, including satellite, UAV, aircraft, ground, and aerial remote sensing data, as well as laboratory monitoring data and seven types of multi-source data fusion. Four remote sensing monitoring techniques were discussed: spectral reflectance analysis, vegetation index analysis, regression model analysis, and spectral differential analysis, and their effects in practical applications were demonstrated through case studies. These methods significantly improve the accuracy and efficiency of pest and disease monitoring. The spectral reflectance analysis method directly reflects the changes in spectral characteristics of crops, and the vegetation index analysis method improves the indicative nature of monitoring by integrating the spectral characteristics of vegetation. The regression model analysis method, on the other hand, provides quantitative estimation of the extent of pests and diseases through mathematical modelling. Spectral differential analysis reveals subtle changes in the spectral profile of the crop and helps in the early identification of pests and diseases.
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Ahmad, Aanis, Dharmendra Saraswat, Aly Gamal, and Gurmukh S. Johal. "Comparison of Deep Learning Models for Corn Disease Region Location, Identification of Disease Type, and Severity Estimation Using Images Acquired From UAS-Mounted and Handheld Sensors." Journal of the ASABE 65, no. 6 (2022): 1433–42. http://dx.doi.org/10.13031/ja.14895.
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Highlights An approach using deep learning was proposed for identifying diseased regions in UAS imagery of corn fields with 97.23% testing accuracy using the VGG16 model. Disease types were identified within the diseased regions with a testing accuracy of 98.85% using the VGG16 model. On the diseased leaves, severity was estimated with a testing accuracy of 94.20% using the VGG16 model. Deep Learning models have the potential to bring efficiency and accuracy to field scouting. Abstract. Accurately locating diseased regions, identifying disease types, and estimating disease severity in corn fields are all connected steps for developing an effective disease management system. Traditional disease management that relied on a manual scouting approach was inefficient. Therefore, the research community is working on developing advanced disease management systems using deep learning. However, most of the past studies used public datasets consisting of images with uniform backgrounds acquired under lab conditions to train deep learning models, thus, limiting their use under field conditions. In addition, limited studies have been conducted for in-field corn disease analysis using Unmanned Aerial System (UAS) imagery. Therefore, UAS and handheld imagery sensors were used in this study to acquire corn disease images from fields located at Purdue University’s Agronomy Center for Research and Education (ACRE) in the summer of 2020. A total of 55 UAS flights were conducted over three different corn fields from June 20 through September 29, resulting in a collection of approximately 59,000 images. A novel three-stage approach was proposed by independently training a total of nine image classification models using three neural network architectures, namely: VGG16, ResNet50, and InceptionV3, for locating diseased regions, identifying disease types, and estimating disease severity under field conditions. Diseased regions were first identified accurately in UAS-acquired corn field imagery by a sliding window and deep learning-based image classification, with testing accuracies of up to 97.23%. Diseased region identification was followed by accurately identifying three common corn diseases, namely Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS), within the diseased regions with testing accuracies of up to 98.85%. Finally, the severity of the NLS disease on leaves was estimated with a testing accuracy of up to 94.20%. The VGG16 model achieved the highest testing accuracies for identifying diseased regions in corn fields, identifying corn disease types, and estimating NLS's severity. This study presents promising results for three main elements of a disease management system and could advance traditional scouting by integrating deep learning with UAS imagery. Keywords: Corn Diseases, Datasets, Deep Learning, Disease Identification, Disease Region Location, Image Classification, Severity Estimation, UAS Imagery.
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Marcu, Ioana, Ana-Maria Drăgulinescu, Cristina Oprea, George Suciu, and Cristina Bălăceanu. "Predictive Analysis and Wine-Grapes Disease Risk Assessment Based on Atmospheric Parameters and Precision Agriculture Platform." Sustainability 14, no. 18 (September 13, 2022): 11487. http://dx.doi.org/10.3390/su141811487.
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In the precision viticulture domain, data recorded by monitoring devices are large-scale processed to improve solutions for grapes’ quality and global production and to offer various recommendations to achieve these goals. Soil-related parameters (soil moisture, structure, etc.) and atmospheric parameters (precipitation, cumulative amount of heat) may facilitate crop diseases occurrence; thus, following predictive analysis, their estimation in vineyards can offer an early-stage warning for farmers and, therefore, suggestions for their prevention and treatment are of particular importance. Using remote sensing devices (e.g., satellites, unmanned vehicles) and proximal sensing methods (e.g., wireless sensor networks (WSNs)), we developed an efficient precision agriculture telemetry platform to provide reliable assessments of atmospheric phenomena periodicity and crop diseases estimation in a vineyard near Bucharest, Romania. The novelty of the materials and methods of this work relies on providing comprehensive preliminary references about monitored parameters to enable efficient, sustainable agriculture. Comparative analyses for two consecutive years illustrate an excellent correlation between cumulative and daily heat, precipitation quantity, and daily evapotranspiration (ET). In addition, the platform proved viable for wine-grapes disease estimation (powdery mildew, grape bunch rot, and grape downy mildew) and treatment recommendations based on the elaborated phenological calendar. Our results, together with continuous monitoring for the upcoming years, may be used as a reference to perform productive, sustainable smart agriculture in terms of yield and crop quality in Romania. In the Conclusion section, we show that farmers and personnel from cooperatives can use this information to make assessments based on the correlation of the available data to avoid critical damage to the wine-grape.
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Zhelezova, Sofia V., Elena V. Pakholkova, Vladislav E. Veller, Mikhail A. Voronov, Eugenia V. Stepanova, Alena D. Zhelezova, Anton V. Sonyushkin, Timur S. Zhuk, and Alexey P. Glinushkin. "Hyperspectral Non-Imaging Measurements and Perceptron Neural Network for Pre-Harvesting Assessment of Damage Degree Caused by Septoria/Stagonospora Blotch Diseases of Wheat." Agronomy 13, no. 4 (April 1, 2023): 1045. http://dx.doi.org/10.3390/agronomy13041045.
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The detection and identification of plant diseases is a fundamental task for sustainable crop production. Septoria tritici and Stagonospora nodorum blotch (STB and SNB) are two of the most common diseases of cereal crops that cause significant economic damage. Both pathogens are difficult to identify at early stages of infection. Determining the degree of the disease at a late infection stage is useful for assessing cereal crops before harvesting, as it allows the assessment of potential yield losses. Hyperspectral sensing could allow for automatic recognition of Septoria harmfulness on wheat in field conditions. In this research, we aimed to collect information on the hyperspectral data on wheat plants with different lesion degrees of STB&SNB and to create and train a neural network for the detection of lesions on leaves and ears caused by STB&SNB infection at the late stage of disease development. Spring wheat was artificially infected twice with Septoria pathogens in the stem elongation stage and in the heading stage. Hyperspectral reflections and brightness measurements were collected in the field on wheat leaves and ears on the 37th day after STB and the 30th day after SNB pathogen inoculation using an Ocean Insight “Flame” VIS-NIR hyperspectrometer. Obtained non-imaging data were pre-treated, and the perceptron model neural network (PNN) was created and trained based on a pairwise comparison of datasets for healthy and diseased plants. Both statistical and neural network approaches showed the high quality of the differentiation between healthy and damaged wheat plants by the hyperspectral signature. A comparison of the results of visual recognition and automatic STB&SNB estimation showed that the neural network was equally effective in the quality of the disease definition. The PNN, based on a neuron model of hyperspectral signature with a spectral step of 6 nm and 2000–4000 value datasets, showed a high quality of detection of the STB&SNB severity. There were 0.99 accuracy, 0.94 precision, 0.89 recall and 0.91 F-score metrics of the PNN model after 10,000 learning epochs. The estimation accuracy of diseased/healthy leaves ranged from 88.1 to 97.7% for different datasets. The accuracy of detection of a light and medium degree of disease was lower (38–66%). This method of non-imaging hyperspectral signature classification could be useful for the identification of the STB and SNB lesion degree identification in field conditions for pre-harvesting crop estimation.
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Anjum, Syeda Samina, Hanamaratti N G, Bandiwaddar T T, Shaila H M, and Chattanavar S N. "Screening Sorghum Breeding Lines for Identification of Resistance Sources for Multiple Fungal Diseases." Journal of Scientific Research and Reports 30, no. 12 (December 10, 2024): 263–70. https://doi.org/10.9734/jsrr/2024/v30i122671.
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Sorghum is a dual purpose crop; both grain and fodder are highly valued for human and animal consumption respectively. However, the crop is vulnerable for many fungal diseases of which downy mildew, rust and grain moldshave become major concern in Dharwad district of North Karnataka revealing impact on sorghum yield. New and alternate sources of host plant resistance are needed for successful management of these diseases. Host plant resistance is the best practice to enable the crop to tolerate diseases which aid in increasing yield. This study aimed to identify the resistant sources against these foliar diseases. About hundred sorghum entries were evaluated under natural unprotected conditions for three continuous years by providing feasible environmental conditions for respective pathogen growth in order to identify resistant sources for downy mildew, rust and grain mold diseases. The incidence of the downy mildew disease was recorded followed by grade for estimation of reaction to downy mildew. However, rust (top four leaves will be considered) and grain mold disease severity was scored using the disease severity rating scale i.e., 1-9 scale at physiological maturity. Among the different entries evaluated, some of the entries viz., SVD-1621, SVD–1503Kand SVD–1411Kshowed multiple disease resistance with reaction varied from resistant to moderately resistant to sorghum downy mildew, rust and grain mold.
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Bhushan, Vidya, and Resh ma. "Plant Leaf Diseases Detection using Deep Learning and Novel CNN." Computer Science & Engineering: An International Journal 15, no. 1 (March 28, 2025): 55–63. https://doi.org/10.5121/cseij.2025.15107.
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" Plant Leaf Diseases Detection Using Deep Learning And Novel CNN" Agricultural products are the basic need of every country. Diseased plants have a serious impact on the country's agricultural and financial resources. Agricultural productivity is important to the economy. Identifying plant diseases in agriculture is important because plant diseases occur naturally. Compared to agriculture, all industries are benefiting from latest technologies. Previous studies have shown that crop yields have decreased due to increased defoliation. This plant disease detection technology can be used to solve this serious problem by analyzing diseases in input images. He explains how to use deep learning techniques to identify and characterize leaf diseases. We use a Convolutional Neural Network (CNN) to obtain accurate estimation and detection of leaf diseases.
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Poland, Jesse A., and Rebecca J. Nelson. "In the Eye of the Beholder: The Effect of Rater Variability and Different Rating Scales on QTL Mapping." Phytopathology® 101, no. 2 (February 2011): 290–98. http://dx.doi.org/10.1094/phyto-03-10-0087.
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The agronomic importance of developing durably resistant cultivars has led to substantial research in the field of quantitative disease resistance (QDR) and, in particular, mapping quantitative trait loci (QTL) for disease resistance. The assessment of QDR is typically conducted by visual estimation of disease severity, which raises concern over the accuracy and precision of visual estimates. Although previous studies have examined the factors affecting the accuracy and precision of visual disease assessment in relation to the true value of disease severity, the impact of this variability on the identification of disease resistance QTL has not been assessed. In this study, the effects of rater variability and rating scales on mapping QTL for northern leaf blight resistance in maize were evaluated in a recombinant inbred line population grown under field conditions. The population of 191 lines was evaluated by 22 different raters using a direct percentage estimate, a 0-to-9 ordinal rating scale, or both. It was found that more experienced raters had higher precision and that using a direct percentage estimation of diseased leaf area produced higher precision than using an ordinal scale. QTL mapping was then conducted using the disease estimates from each rater using stepwise general linear model selection (GLM) and inclusive composite interval mapping (ICIM). For GLM, the same QTL were largely found across raters, though some QTL were only identified by a subset of raters. The magnitudes of estimated allele effects at identified QTL varied drastically, sometimes by as much as threefold. ICIM produced highly consistent results across raters and for the different rating scales in identifying the location of QTL. We conclude that, despite variability between raters, the identification of QTL was largely consistent among raters, particularly when using ICIM. However, care should be taken in estimating QTL allele effects, because this was highly variable and rater dependent.
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Ronning, C. M., D. M. Harkins, R. J. Schnell, and L. H. Purdy. "ESTIMATION OF GENETIC RELATIONSHIPS IN THEOBROMA CACAO." HortScience 26, no. 6 (June 1991): 691C—691. http://dx.doi.org/10.21273/hortsci.26.6.691c.
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Cacao is an important crop in the tropics, but its breeding has been hampered by a lack of understanding of its genetics. One result of this has been the introduction of “hybrid” trees which did not perform predictably under various environmental conditions. We are studying the inheritance of isoenzyme, RFLP, and Random Amplified Polymorphic DNA (RAPD™) markers in order to estimate the genetic relationships among and between populations. Our objectives include determining if any linkage exists between these molecular markers and witches' broom (Crinipellis perniciosa) resistance, a major disease of cacao.
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Mavridou, Efthimia, Eleni Vrochidou, George A. Papakostas, Theodore Pachidis, and Vassilis G. Kaburlasos. "Machine Vision Systems in Precision Agriculture for Crop Farming." Journal of Imaging 5, no. 12 (December 7, 2019): 89. http://dx.doi.org/10.3390/jimaging5120089.
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Machine vision for precision agriculture has attracted considerable research interest in recent years. The aim of this paper is to review the most recent work in the application of machine vision to agriculture, mainly for crop farming. This study can serve as a research guide for the researcher and practitioner alike in applying cognitive technology to agriculture. Studies of different agricultural activities that support crop harvesting are reviewed, such as fruit grading, fruit counting, and yield estimation. Moreover, plant health monitoring approaches are addressed, including weed, insect, and disease detection. Finally, recent research efforts considering vehicle guidance systems and agricultural harvesting robots are also reviewed.
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Nita, M., M. A. Ellis, and L. V. Madden. "Reliability and Accuracy of Visual Estimation of Phomopsis Leaf Blight of Strawberry." Phytopathology® 93, no. 8 (August 2003): 995–1005. http://dx.doi.org/10.1094/phyto.2003.93.8.995.
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Six different individuals (raters) assessed the severity of Phomopsis leaf blight on strawberry leaflets in five experimental repetitions over 2 years by making a direct visual estimation of the percentage of diseased area of each leaflet or by using the Horsfall-Barratt (H-B) disease scale. Intra-rater and inter-rater reliability and accuracy were determined, and then the relationship between visually estimated severity values and actual severity values was evaluated. Agreement in estimated disease severity values between assessment times by the same raters (i.e., intra-rater reliability), and agreement in disease severity values among raters at a single assessment time (i.e., inter-rater reliability), were both high, with most correlation coefficients being greater than 0.85. The intra-class correlation for overall agreement among raters ranged from 0.80 to 0.96 for the five repetitions. Based on the concordance coefficient calculated for each rater in each repetition, agreement between estimated and actual severity (i.e., accuracy) was somewhat lower than reliability. The relationship between estimated and actual severity was linear, and there was a slight trend to overestimate disease severity. The H-B scale was not more reliable or accurate than direct estimation of severity, and the linear relationship between estimated and actual severity did not support the principles underling the H-B scale. Both size of leaflets and number of lesions per leaflet slightly affected the error in estimate of disease severity.
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Obsie, Efrem Yohannes, Hongchun Qu, Yong-Jiang Zhang, Seanna Annis, and Francis Drummond. "Yolov5s-CA: An Improved Yolov5 Based on the Attention Mechanism for Mummy Berry Disease Detection." Agriculture 13, no. 1 (December 27, 2022): 78. http://dx.doi.org/10.3390/agriculture13010078.
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Early detection and accurately rating the level of plant diseases plays an important role in protecting crop quality and yield. The traditional method of mummy berry disease (causal agent: Monilinia vaccinii-corymbosi) identification is mainly based on field surveys by crop protection experts and experienced blueberry growers. Deep learning models could be a more effective approach, but their performance is highly dependent on the volume and quality of labeled data used for training so that the variance in visual symptoms can be incorporated into a model. However, the available dataset for mummy berry disease detection does not contain enough images collected and labeled from a real-field environment essential for making highly accurate models. Complex visual characteristics of lesions due to overlapping and occlusion of plant parts also pose a big challenge to the accurate estimation of disease severity. This may become a bigger issue when spatial variation is introduced by using sampling images derived from different angles and distances. In this paper, we first present the “cut-and-paste” method for synthetically augmenting the available dataset by generating additional annotated training images. Then, a deep learning-based object recognition model Yolov5s-CA was used, which integrates the Coordinated Attention (CA) module on the Yolov5s backbone to effectively discriminate useful features by capturing channel and location information. Finally, the loss function GIoU_loss was replaced by CIoU_loss to improve the bounding box regression and localization performance of the network model. The original Yolov5s and the improved Yolov5s-CA network models were trained on real, synthetic, and combined mixed datasets. The experimental results not only showed that the performance of Yolov5s-CA network model trained on a mixed dataset outperforms the baseline model trained with only real field images, but also demonstrated that the improved model can solve the practical problem of diseased plant part detection in various spatial scales with possible overlapping and occlusion by an overall precision of 96.30%. Therefore, our model is a useful tool for the estimation of mummy berry disease severity in a real field environment.
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Guan, Qiang, Shicheng Qiao, Shuai Feng, and Wen Du. "Investigation of Peanut Leaf Spot Detection Using Superpixel Unmixing Technology for Hyperspectral UAV Images." Agriculture 15, no. 6 (March 11, 2025): 597. https://doi.org/10.3390/agriculture15060597.
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Leaf spot disease significantly impacts peanut growth. Timely, effective, and accurate monitoring of leaf spot severity is crucial for high-yield and high-quality peanut production. Hyperspectral technology from unmanned aerial vehicles (UAVs) is widely employed for disease detection in agricultural fields, but the low spatial resolution of imagery affects accuracy. In this study, peanuts with varying levels of leaf spot disease were detected using hyperspectral images from UAVs. Spectral features of crops and backgrounds were extracted using simple linear iterative clustering (SLIC), the homogeneity index, and k-means clustering. Abundance estimation was conducted using fully constrained least squares based on a distance strategy (D-FCLS), and crop regions were extracted through threshold segmentation. Disease severity was determined based on the average spectral reflectance of crop regions, utilizing classifiers such as XGBoost, the MLP, and the GA-SVM. Results indicate that crop spectra extracted using the superpixel-based unmixing method effectively captured spectral variability, leading to more accurate disease detection. By optimizing threshold values, a better balance between completeness and the internal variability of crop regions was achieved, allowing for the precise extraction of crop regions. Compared to other unmixing methods and manual visual interpretation techniques, the proposed method achieved excellent results, with an overall accuracy of 89.08% and a Kappa coefficient of 85.42% for the GA-SVM classifier. This method provides an objective, efficient, and accurate solution for detecting peanut leaf spot disease, offering technical support for field management with promising practical applications.
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Delp, B. R. "Evaluation of Field Sampling Techniques for Estimation of Disease Incidence." Phytopathology 76, no. 12 (1986): 1299. http://dx.doi.org/10.1094/phyto-76-1299.
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Zhou, Leijinyu, Hongbo Wu, Tingting Jing, Tianhao Li, Jinsheng Li, Lijuan Kong, and Lina Zhou. "Estimation of Relative Chlorophyll Content in Lettuce (Lactuca sativa L.) Leaves under Cadmium Stress Using Visible—Near-Infrared Reflectance and Machine-Learning Models." Agronomy 14, no. 3 (February 22, 2024): 427. http://dx.doi.org/10.3390/agronomy14030427.
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Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (Rv2) and root mean squared error (RMSEv) values of 0.87 and 1.21, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.
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Nainwal, Mukta, Ravi Kiran, KP Singh, Rajeev Ranjan, and AS Nain. "Estimation of AUDPC (Area under disease progressive curve) of RAB (Rhizoctonia aerial blight) disease for epidemiological studied in soybean crop." International Journal of Chemical Studies 8, no. 3 (May 1, 2020): 2702–4. http://dx.doi.org/10.22271/chemi.2020.v8.i3am.9621.
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Ahmad, Ali, Javier Ordoñez, Pedro Cartujo, and Vanesa Martos. "Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability." Agronomy 11, no. 1 (December 23, 2020): 7. http://dx.doi.org/10.3390/agronomy11010007.
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The current COVID-19 global pandemic has amplified the pressure on the agriculture sector, inciting the need for sustainable agriculture more than ever. Thus, in this review, a sustainable perspective of the use of remotely piloted aircraft (RPA) or drone technology in the agriculture sector is discussed. Similarly, the types of cameras (multispectral, thermal, and visible), sensors, software, and platforms frequently deployed for ensuring precision agriculture for crop monitoring, disease detection, or even yield estimation are briefly discoursed. In this regard, vegetation indices (VIs) embrace an imperative prominence as they provide vital information for crop monitoring and decision-making, thus a summary of most commonly used VIs is also furnished and serves as a guide while planning to collect specific crop data. Furthermore, the establishment of significant applications of RPAs in livestock, forestry, crop monitoring, disease surveillance, irrigation, soil analysis, fertilization, crop harvest, weed management, mechanical pollination, crop insurance and tree plantation are cited in the light of currently available literature in this domain. RPA technology efficiency, cost and limitations are also considered based on the previous studies that may help to devise policies, technology adoption, investment, and research activities in this sphere.
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Fahey, Thomas, Hai Pham, Alessandro Gardi, Roberto Sabatini, Dario Stefanelli, Ian Goodwin, and David William Lamb. "Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops." Sensors 21, no. 1 (December 29, 2020): 171. http://dx.doi.org/10.3390/s21010171.
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In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.
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Mojerlou, Shideh, Naser Safaie, Azizollah Alizadeh, and Fatemeh Khelghatibana. "Measuring and Modeling Crop Loss of Wheat Caused by Septoria Leaf Blotch in Seven Cultivars and Lines in Iran." Journal of Plant Protection Research 49, no. 3 (September 1, 2009): 257–62. http://dx.doi.org/10.2478/v10045-009-0039-8.
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Measuring and Modeling Crop Loss of Wheat Caused by Septoria Leaf Blotch in Seven Cultivars and Lines in IranSeptoria leaf blotch caused bySeptoria tritici, is one of the most important diseases of wheat worldwide including Iran. To determine yield reduction caused by this disease in Golestan province, field experiments were carried out in randomized complete block design with four replications and five wheat cvs. Tajan, Zagros, Shiroodi, Koohdasht, Shanghai and two lines N-80-6 and N-80-19 at Gorgan Research Station. Artificial inoculation was performed using spore suspension at three growth stages (Zadoks scale) including tillering (GS 37), stem elongation (GS 45) and flag leaf opening (GS 53). Control plots were sprayed with water. In this study, the 1 000 kernel weight (TKW), grain yield and area under disease progress curve (AUDPC) during growth season were measured. Statistical analysis showed that the levels of yield reduction was different in various studied wheat cultivars and lines and was reduced by 30 to 50%. The highest losses were observed for cvs. Zagros and Tajan with 48.86% and 47.41% of grain yield reduction, respectively. There was a positive correlation between grain yield reduction and AUDPC. The results of crop loss modelling using integral and multiple point regression models showed that the integral model (L = 1230.91+1.37AUDPC) in which AUDPC and crop loss percentages were independent and dependent variables, respectively, could explain more than 95% of AUDPC variations in relation to crop loss in all cultivars in two years. In the study of integral model for each cultivar, cv. Shiroodi showed the highest fitness. In multiple point models, disease severity at various dates was considered as independent variables and crop loss percentage as dependent variable. This model with the highest coefficient of determination had the best fitness for crop loss estimation. Besides, the results showed that the disease severity at GS37, GS53 and GS91 stages (Zadok's scale) was more important for crop loss prediction than that in other phenological stages.
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Kremneva, O. Yu, and K. E. Gasiyan. "The use of spore-catching equipment detecting diseases of grain crops (review)." Grain Economy of Russia, no. 1 (March 27, 2023): 94–98. http://dx.doi.org/10.31367/2079-8725-2023-84-1-94-98.
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In order to manage the phytosanitary situation on the sowings and get the maximum yields of high quality, at first it is necessary to estimate the condition of the protected crop. Based on this estimation, in future it is possible to establish the most effective and economically justified protection system. Grain crops are the most important strategic crops that ensure food security around the world. According to the FAO the world crop losses caused by pests have reached up to 40 % where fungal pathogens have played the most significant role. Therefore, the most important task of phytosanitary monitoring is the timely detection and identification of the disease before the beginning of symptoms at the earliest stages of pathogen development, which becomes possible when the infectious beginning of the disease has been detected. When monitoring fungal diseases, spore-catching equipment allow this issue to be solved. The purpose of the current review was to describe the existing developments of spore-catching equipment for monitoring grain crop diseases and to identify promising areas for using devices on crops to improve protective measures’ efficiency. The introduction has briefly described the classical methods of monitoring and the relatively new methods currently used. In the main part there has been considered a spore-catching equipment developed and used both in foreign and domestic practice. There have been analyzed the methods of using spore-catching equipment and given the examples of the use of these devices in monitoring crop diseases. In the conclusions there have been summarized the trends in the development of technical support for phytosanitary monitoring and shown areas that have been found promising for further research.
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Palma, David, Franco Blanchini, and Pier Luca Montessoro. "A system-theoretic approach for image-based infectious plant disease severity estimation." PLOS ONE 17, no. 7 (July 26, 2022): e0272002. http://dx.doi.org/10.1371/journal.pone.0272002.
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The demand for high level of safety and superior quality in agricultural products is of prime concern. The introduction of new technologies for supporting crop management allows the efficiency and quality of production to be improved and, at the same time, reduces the environmental impact. Common strategies to disease control are mainly oriented on spraying pesticides uniformly over cropping areas at different times during the growth cycle. Even though these methodologies can be effective, they present a negative impact in ecological and economic terms, introducing new pests and elevating resistance of the pathogens. Therefore, consideration for new automatic and accurate along with inexpensive and efficient techniques for the detection and severity estimation of pathogenic diseases before proper control measures can be suggested is of great realistic significance and may reduce the likelihood of an infection spreading. In this work, we present a novel system-theoretic approach for leaf image-based automatic quantitative assessment of pathogenic disease severity regardless of disease type. The proposed method is based on a highly efficient and noise-rejecting positive non-linear dynamical system that recursively transforms the leaf image until only the symptomatic disease patterns are left. The proposed system does not require any training to automatically discover the discriminative features. The experimental setup allowed to assess the system ability to generalise symptoms detection beyond any previously seen conditions achieving excellent results. The main advantage of the approach relies in the robustness when dealing with low-resolution and noisy images. Indeed, an essential issue related to digital image processing is to effectively reduce noise from an image whilst keeping its features intact. The impact of noise is effectively reduced and does not affect the final result allowing the proposed system to ensure a high accuracy and reliability.
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Corrales, David Camilo, Apolinar Figueroa Casas, Agapito Ledezma, and Juan Carlos Corrales. "Two-Level Classifier Ensembles for Coffee Rust Estimation in Colombian Crops." International Journal of Agricultural and Environmental Information Systems 7, no. 3 (July 2016): 41–59. http://dx.doi.org/10.4018/ijaeis.2016070103.
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Rust is a disease that leads to considerable losses in the worldwide coffee industry. There are many contributing factors to the onset of coffee rust e.g. Crop management decisions and the prevailing weather. In Colombia the coffee production has been considerably reduced by 31% on average during the epidemic years compared with 2007. Recent research efforts focus on detection of disease incidence using simple classifiers. Authors in the computer field propose alternatives for improve the outcomes, making use of techniques that combine classifiers named ensemble methods. Therefore they proposed two-level classifier ensembles for coffee rust estimation in Colombian crops using Back Propagation Neural Networks, Regression Tree M5 and Support Vector Regression. Their ensemble approach outperformed the classical approaches as simple classifiers and ensemble methods in terms of Pearson's Correlation Coefficient, Mean Absolute Error and Root Mean Squared Error.
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Ansari, T. H., M. Ahmed, S. Akter, M. S. Mian, M. A. Latif, and M. Tomita. "Estimation of Rice Yield Loss Using a Simple Linear Regression Model for Bacterial Blight Disease." Bangladesh Rice Journal 23, no. 1 (March 23, 2020): 73–79. http://dx.doi.org/10.3329/brj.v23i1.46083.
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Field experiment was carried out in hot and humid summer (Transplanted Aus) season to realize the yield loss of a susceptible rice variety Purbachi inoculated with bacterial blight (BB). Treatments consist of BB inoculations at different crop growth stages like maximum tillering (MT), panicle initiation (PI), booting (Bt), flowering and heading stages differently including a control (no BB inoculation). Disease severity index (DSI) was measured at 14 days after inoculation (DAI) and harvest. Data on 1000-grain-weight and yield was recorded at harvest. Significant variation on DSI was observed among different BB inoculated crop growth stages. MT, PI and Boot stage inoculations showed similar (DSI 7.1-8.0) but higher DSI than flowering and heading stages inoculation (3.2-5.3) even control (0.00) at 14 DAI. However, all the treatments showed similar DSI 9.0 at harvest. Bacterial blight can affect the grain weight to some extent although it was insignificant among the treatments (0.1-4.5%). DSI showed negative correlation with 1000-grain weight (r=-0.77*) and similarly with the yield (r=-0.97**). The yield ranged from 2.4-3.4 t/ha among the treatments. The yield loss was observed 5.8-30.4% in the BB inoculated treatments. MT, PI and Boot stages inoculation affected the yield much resulting 21-30.4% yield loss. It could be concluded that a susceptible variety can be affected with significant yield loss up to 30.4% with severe outbreak of B B. A simple regression equation = 4.09-0.211X( = Yield, X = BB severity score) is suggested for the prediction of yield loss in susceptible variety in summer season.
Bangladesh Rice j. 2019, 23(1): 73-79
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Francelino, Helenilson Oliveira, Marcelo Vivas, Ramon de Moraes, Júlio Cesar Gradice Saluci, Janiele Maganha Silva Vivas, Derivaldo Pureza da Cruz, Geraldo de Amaral Gravina, and Silvaldo Felipe da Silveira. "Diagrammatic scale for the quantification of black spot severity in papaya leaves." Acta Scientiarum. Agronomy 45 (August 22, 2023): e60970. http://dx.doi.org/10.4025/actasciagron.v45i1.60970.
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Black spot (Asperisporium caricae) is one of the main foliar fungal diseases of papaya crops. This disease acts directly on leaves and fruits causing leaf area reduction and fruit deterioration. The quantification of diseases is a fundamental part of the disease management and control process; therefore, a scale is required to help quantify black spot disease. The objective of this work was to propose a standardized methodology to quantify black spot severity in papaya leaves. A scale was developed considering the maximum and minimum values of the disease in the field that included eight levels of severity: 0.1, 0.3, 0.6, 1.0, 2.3, 5.0, 10.0, and 20.0%. Without the aid of a scale the disease is often overestimated, with absolute errors of approximately 75%. When the scale was used, 100% of the evaluators showed improved accuracy and precision, and absolute error was reduced to the 10% range. The scale also provided good repeatability and high reproducibility. The use of the scale provided an improvement in the R2 values, with mean values of 93 and 92 in the second and third evaluations, respectively, demonstrating that the scale is useful for different aspects of the pathosystem of A. caricae, such as for determining the efficiency of fungicides, characterization of varietal resistance, construction of the disease progression curve, and estimation of damage.
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RANJAN, PRAGYA, K. K. GANGOPADHYAY, MANAS KUMAR BAG, ANIRBAN ROY, R. SRIVASTAVA, R. BHARDWAJ, and M. DUTTA. "Evaluation of cucumber (Cucumis sativus) germplasm for agronomic traits and disease resistance and estimation of genetic variability." Indian Journal of Agricultural Sciences 85, no. 2 (February 12, 2015): 234–39. http://dx.doi.org/10.56093/ijas.v85i2.46516.
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The material used in the present study is of diverse nature and can be used in the breeding programme for development of improved genotypes in cucumber (Cucumis sativus L.). The unique accessions identified in this study can be useful as genetic stocks. The superior genotypes for fruit trait variability combined with disease resistance may assist the breeders in identifying populations with desired traits for inclusion in crop improvement programme.
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Ovesná, Jaroslava, Michail D. Kaminiaris, Zisis Tsiropoulos, Rosemary Collier, Alex Kelly, Jonathan De Mey, and Sabien Pollet. "Applicability of Smart Tools in Vegetable Disease Diagnostics." Agronomy 13, no. 5 (April 25, 2023): 1211. http://dx.doi.org/10.3390/agronomy13051211.
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Various diseases and pests cause serious damage to vegetable crops during the growing season and after harvesting. Growers attempt to minimize losses by protecting their crops, starting with seed and seedling treatments and followed by monitoring their stands. In many cases, synthetic pesticide treatments are applied. Integrated pest management is currently being employed to minimize the impact of pesticides upon human health and the environment. Over the last few years, “smart” approaches have been developed and adopted in practice to predict, detect, and quantify phytopathogen occurrence and contamination. Our review assesses the currently available ready-to-use tools and methodologies that operate via visual estimation, the detection of proteins and DNA/RNA sequences, and the utilization of brand-new innovative approaches, highlighting the availability of solutions that can be used by growers during the process of diagnosing pathogens.
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Shahi, Tej Bahadur, Cheng-Yuan Xu, Arjun Neupane, and William Guo. "Machine learning methods for precision agriculture with UAV imagery: a review." Electronic Research Archive 30, no. 12 (2022): 4277–317. http://dx.doi.org/10.3934/era.2022218.
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<abstract> <p>Because of the recent development in advanced sensors, data acquisition platforms, and data analysis methods, unmanned aerial vehicle (UAV) or drone-based remote sensing has gained significant attention from precision agriculture (PA) researchers. The massive amount of raw data collected from such sensing platforms demands large-scale data processing algorithms such as machine learning and deep learning methods. Therefore, it is timely to provide a detailed survey that assimilates, categorises, and compares the performance of various machine learning and deep learning methods for PA. This paper summarises and synthesises the recent works using a general pipeline of UAV-based remote sensing for precision agriculture research. We classify the different features extracted from UAV imagery for various agriculture applications, showing the importance of each feature for the performance of the crop model and demonstrating how the multiple feature fusion can improve the models' performance. In addition, we compare and contrast the performances of various machine learning and deep learning models for three important crop trait estimations: yield estimation, disease detection and crop classification. Furthermore, the recent trends in applications of UAVs for PA are briefly discussed in terms of their importance, and opportunities. Finally, we recite the potential challenges and suggest future avenues of research in this field.</p> </abstract>
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Mara, Duncan, and Andrew Sleigh. "Estimation of norovirus and Ascaris infection risks to urban farmers in developing countries using wastewater for crop irrigation." Journal of Water and Health 8, no. 3 (March 9, 2010): 572–76. http://dx.doi.org/10.2166/wh.2010.097.
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A quantitative microbial risk analysis—Monte Carlo method was used to estimate norovirus and Ascaris infection risks to urban farmers in developing countries watering their crops with wastewater. For a tolerable additional disease burden of≤10−4 DALY loss per person per year (pppy), equivalent to 1 percent of the diarrhoeal disease burden in developing countries, a norovirus reduction of 1–2 log units and an Ascaris egg reduction to 10–100 eggs per litre are required. These are easily achieved by minimal wastewater treatment—for example, a sequential batch-fed three tank/pond system. Hygiene improvement through education and regular deworming are essential complementary inputs to protect the health of urban farmers.
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Blekos, Achilleas, Konstantinos Chatzis, Martha Kotaidou, Theocharis Chatzis, Vassilios Solachidis, Dimitrios Konstantinidis, and Kosmas Dimitropoulos. "A Grape Dataset for Instance Segmentation and Maturity Estimation." Agronomy 13, no. 8 (July 27, 2023): 1995. http://dx.doi.org/10.3390/agronomy13081995.
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Grape maturity estimation is vital in precise agriculture as it enables informed decision making for disease control, harvest timing, grape quality, and quantity assurance. Despite its importance, there are few large publicly available datasets that can be used to train accurate and robust grape segmentation and maturity estimation algorithms. To this end, this work proposes the CERTH grape dataset, a new sizeable dataset that is designed explicitly for evaluating deep learning algorithms in grape segmentation and maturity estimation. The proposed dataset is one of the largest currently available grape datasets in the literature, consisting of around 2500 images and almost 10 k grape bunches, annotated with masks and maturity levels. The images in the dataset were captured under various illumination conditions and viewing angles and with significant occlusions between grape bunches and leaves, making it a valuable resource for the research community. Thorough experiments were conducted using a plethora of general object detection methods to provide a baseline for the future development of accurate and robust grape segmentation and maturity estimation algorithms that can significantly advance research in the field of viticulture.
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SINGH, A. K., R. RAI, C. P. SRIVASTAVA, B. D. SINGH, C. KUSHWAHA, and R. CHAND. "A quantitative analysis of rust (Uromyces fabae) resistance in pea (Pisum sativum) using RILs." Indian Journal of Agricultural Sciences 82, no. 2 (February 7, 2012): 190–2. http://dx.doi.org/10.56093/ijas.v82i2.15301.
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In a quantitative analysis of pea rust resistance using a RIL population, heritability estimates for disease severity and AUDPC were found to be 0.90 and 0.93, respectively. High heritability indicated that selection for pea rust resistance can be made under polyhouse conditions using either disease severity or AUDPC as disease reaction estimators. Average degree of dominance (ADD) for resistance to U. fabae was 0.11 and 0.14 for disease severity and AUDPC, respectively, indicating that the genes controlling U. fabae resistance exhibited a low degree of incomplete dominance. Estimates of the minimum number of effective genes conferring resistance to pea rust, using three different methods of estimation, varied from two to three.
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Suryanto, Agus, Kuswanto Kuswanto, SM Sitompul, and Astanto Kasno. "Estimation Of Number And Genes Actions Of Cpmmv (Cowpea Mild Mottle Virus) Disease Resistance Genes On Soybean Crop." IOSR Journal of Agriculture and Veterinary Science 7, no. 5 (2014): 51–56. http://dx.doi.org/10.9790/2380-07535156.
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Bock, C. H., P. E. Parker, A. Z. Cook, and T. R. Gottwald. "Characteristics of the Perception of Different Severity Measures of Citrus Canker and the Relationships Between the Various Symptom Types." Plant Disease 92, no. 6 (June 2008): 927–39. http://dx.doi.org/10.1094/pdis-92-6-0927.
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Citrus canker is a disease of citrus and is caused by the bacterial pathogen Xanthomonas citri subsp. citri. Ways of managing the disease are being sought, and accurate, precise, reproducible disease assessment is needed for monitoring epidemics. The objective of this study was to investigate the characteristics of visual assessment of citrus canker symptoms compared with actual disease measured using image analysis (IA). Images of 210 citrus leaves with a range of incidence and severity of citrus canker were assessed by three plant pathologists (VR1-3) and by IA. The number of lesions (L), % area necrotic (%AN), and % area necrotic+chlorotic (%ANC) were assessed. The best relationships were found between %AN and %ANC (r2 = 0.41 to 0.87), and the worst between L and %AN (r2 = 0.27 to 0.66). Bland-Altman plots showed various sources of rater error in assessments, including under- and over-estimation, proportional error, and heterogeneity of variation dependent on actual disease magnitude. There was a tendency to overestimate area diseased, but not lesion counts, and this tendency was pronounced at lower disease severity, with a leaf having more lesions tending to be assessed as having greater area infected compared with a leaf with fewer lesions but equal actual area infected. The rater estimations of disease were less accurate or precise with increasing actual disease severity as indicated by the fit of a normal probability density function—the incidence of extreme values increases with increasing actual disease. For example, for %ANC the kurtosis of the distribution ranged from 17.92 to 1.18, 0.51, and 0.22 in actual disease category ranges of 0 to 10, 11 to 20, 21 to 30, and 31 to 40% area infected, respectively. The log variance of the estimates plotted against log actual disease for all three raters over two assessment occasions gave a linear relationship for L, %AN, and %ANC (r2 = 0.74, 0.65, and 0.74, respectively). Training should improve the accuracy, precision, and reproducibility of raters, and knowledge of the characteristics of disease assessment should help develop and target the training more appropriately and address specific causes and sources of error.
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Liang, Qiaokang, Shao Xiang, Yucheng Hu, Gianmarc Coppola, Dan Zhang, and Wei Sun. "PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network." Computers and Electronics in Agriculture 157 (February 2019): 518–29. http://dx.doi.org/10.1016/j.compag.2019.01.034.
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Jalalifar, Reza, Atefeh Sabouri, Sedigheh Mousanejad, and Ahmad Reza Dadras. "Estimation of Genetic Parameters and Identification of Leaf Blast-Resistant Rice RILs Using Cluster Analysis and MGIDI." Agronomy 13, no. 11 (October 29, 2023): 2730. http://dx.doi.org/10.3390/agronomy13112730.
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Rice blast disease, caused by the fungus Magnaporthe oryzae, poses a significant threat to rice cultivation. One effective way to deal with this disease is to identify and introduce resistant varieties using different breeding methods. This study utilized a population of 153 recombinant inbred lines (RILs) derived from the crossing of the Shahpasand (SH) and IR28 varieties, characterized by susceptibility and resistance to leaf blast, respectively. In combination with 12 control varieties, these genotypes were subjected to an extensive evaluation of disease severity (5 stages), the area under the disease progress curve (AUDPC), type, and the infection rate in 2021 and 2022. Analysis of variance revealed significant genetic variation, highlighting the potential of the RIL population for identifying and selecting resistant lines. Employing cluster analysis and the multi-trait genotype-ideotype distance index (MGIDI), 17 lines were identified as the most resistant over a two-year evaluation period. The average AUDPC for these resistant lines was estimated at 2.435 ± 0.114, and lines 17 and 111 had the lowest AUDPC (1.526 and 1.630, respectively) and showed the least infection in two years. Conversely, lines 42 and 43 showed the highest AUDPC values (255.312 and 248.209) along with heightened sensitivity. The use of MGIDI yielded a substantial selection differential (SD) of −59.12% for traits related to leaf blast disease resistance, demonstrating the effectiveness of this method. Furthermore, new recombinant populations are expected to be developed in future plant breeding projects by crossing the most susceptible and resistant lines, which will be new sources of resistance to this disease.
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Lynch, J. P., R. Fealy, D. Doyle, L. Black, and J. Spink. "Assessment of water-limited winter wheat yield potential at spatially contrasting sites in Ireland using a simple growth and development model." Irish Journal of Agricultural and Food Research 56, no. 1 (September 19, 2017): 65–76. http://dx.doi.org/10.1515/ijafr-2017-0007.
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AbstractAlthough Irish winter wheat yields are among the highest globally, increases in the profitability of this crop are required to maintain its economic viability. However, in order to determine if efforts to further increase Irish wheat yields are likely to be successful, an accurate estimation of the yield potential is required for different regions within Ireland. A winter wheat yield potential model (WWYPM) was developed, which estimates the maximum water-limited yield achievable, within the confines of current genetic resources and technologies, using parameters for winter wheat growth and development observed recently in Ireland and a minor amount of daily meteorological input (maximum and minimum daily temperature, total daily rainfall and total daily incident radiation). The WWYPM is composed of three processes: (i) an estimation of potential green area index, (ii) an estimation of light interception and biomass accumulation and (iii) an estimation of biomass partitioning to grain yield. Model validation indicated that WWYPM estimations of water-limited yield potential (YPw) were significantly related to maximum yields recorded in variety evaluation trials as well as regional average and maximum farm yields, reflecting the model’s sensitivity to alterations in the climatic environment with spatial and seasonal variations. Simulations of YPw for long-term average weather data at 12 sites located at spatially contrasting regions of Ireland indicated that the typical YPw varied between 15.6 and 17.9 t/ha, with a mean of 16.7 t/ha at 15% moisture content. These results indicate that the majority of sites in Ireland have the potential to grow high-yielding crops of winter wheat when the effects of very high rainfall and other stresses such as disease incidence and nutrient deficits are not considered.
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Goswami, Anwesha, Pritam Phonglo, Marjana Medhi, Priyam Hazarika, Dikhasmita Bezbaruah, and Nikee Chutia. "Drone-Based Sensing and Imaging for Fruit Crop Monitoring: A Review." Archives of Current Research International 24, no. 12 (December 29, 2024): 325–32. https://doi.org/10.9734/acri/2024/v24i121023.
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The global fruit production industry has been suffering from numerous problems in the yield, quality, and food safety context. The use of drone-based sensing and imaging technologies has emerged as a promising approach for monitoring fruit crops, enabling real-time assessment of crop health, growth, and development. Monitoring fruit crops helps identify areas of improvement and makes decisions based on data. This review focuses on the various sensor technologies utilized in drone-based fruit crop monitoring, including RGB, multispectral, hyperspectral, thermal, and LiDAR sensors. The applications of these sensors are discussed, including yield estimation and prediction, crop growth monitoring, disease detection and diagnosis, pest detection and management, nutrient deficiency detection, and water stress monitoring. The review highlights the advantages and limitations of each sensor technology, as well as the challenges associated with data processing and analysis. Case studies demonstrate the effectiveness of drone-based sensing and imaging in fruit crop monitoring, and future directions are discussed, including the integration of sensor technologies with other precision agriculture tools and the development of specialized sensors and cameras. Standardization and best practices are emphasized as crucial for the widespread adoption of drone-based sensing and imaging in fruit crop monitoring.
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Bailey, D. J., and C. A. Gilligan. "Modeling and Analysis of Disease-Induced Host Growth in the Epidemiology of Take-All." Phytopathology® 94, no. 5 (May 2004): 535–40. http://dx.doi.org/10.1094/phyto.2004.94.5.535.
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Epidemiological modeling, together with parameter estimation to experimental data, was used to examine the contribution of disease-induced root growth to the spread of take-all in wheat. Production of roots from plants grown in the absence of disease was compared with production of those grown in the presence of disease and the precise form of diseaseinduced growth was examined by fitting a mechanistic model to data describing change in the number of infected and susceptible roots over time from a low and a high density of inoculum. During the early phase of the epidemic, diseased plants produced more roots than their noninfected counterparts. However, as the epidemic progressed, the rate of root production for infected plants slowed so that by the end of the epidemic, and depending on inoculum density, infected plants had fewer roots than uninfected plants. The dynamical change in the numbers of infected and susceptible roots over time could only be explained by the mechanistic model when allowance was made for disease-induced root growth. Analysis of the effect of disease-induced root production on the spread of disease using the model suggests that additional roots produced early in the epidemic serve only to reduce the proportion of diseased roots. However, as the epidemic switches from primary to secondary infection, these roots perform an active role in the transmission of disease. Some consequence of disease-induced root growth for field epidemics is discussed.
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OLESEN, J. E., L. N. JØRGENSEN, and J. V. MORTENSEN. "Irrigation strategy, nitrogen application and fungicide control in winter wheat on a sandy soil. II. Radiation interception and conversion." Journal of Agricultural Science 134, no. 1 (January 2000): 13–23. http://dx.doi.org/10.1017/s0021859699007285.
Full textAbstract:
Data from a three factor experiment carried out during two years were used to analyse the effects of drought, nitrogen and disease on light interception (IPAR) and radiation use efficiency (RUE) in winter wheat (Triticum aestivum L.). The factors in the experiment comprised four irrigation strategies including no irrigation, three nitrogen levels providing 67, 83 and 100% of the recommended nitrogen rate, and two strategies for control of leaf diseases (with and without fungicides). Light interception was estimated from weekly measurements of crop spectral reflectance. This method was compared with estimates derived from crop area index measured by plant samples or by using the LAI2000 instrument. There was a good correspondence between the different methods before anthesis, but an overestimation of light interception with the methods using crop area index after anthesis due to an increase in non-photosynthetic active leaf area. Irrigation increased both IPAR and RUE. The relative increase in IPAR for irrigation was greater than the relative increase in RUE in the first year, whereas they were of similar size in the second year. The differences between the years could be attributed to changes in timing of the drought relative to crop ontogenesis. Increasing nitrogen rate increased IPAR, but caused a small decrease in RUE in both years. This reduction in RUE with increasing nitrogen concentration in leaves was also found to be significant when disease levels and drought effects were included in a multiple linear regression. Fungicide application increased IPAR in both years, but RUE was only significantly reduced by disease in the first year, where mildew dominated the trial. The data were also used to estimate the coefficients of partitioning of dry matter to grains before and after anthesis. About 40% of dry matter produced before anthesis and about 60% after anthesis was estimated to contribute to grain yield. The low fraction after anthesis is probably due to the fact that it was not possible to estimate changes in RUE with time, which may lead to biases in the estimation of partitioning coefficients.
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Wang, Yuxuan, Shamaila Zia-Khan, Sebastian Owusu-Adu, Thomas Miedaner, and Joachim Müller. "Early Detection of Zymoseptoria tritici in Winter Wheat by Infrared Thermography." Agriculture 9, no. 7 (July 2, 2019): 139. http://dx.doi.org/10.3390/agriculture9070139.
Full textAbstract:
The use of thermography as a means of crop water status estimation is based on the assumption that a sufficient amount of soil moisture enables plants to transpire at potential rates resulting in cooler canopy than the surrounding air temperature. The same principle is applied in this study where the crop transpiration changes occur because of the fungal infection. The field experiment was conducted where 25 wheat genotypes were infected with Zymoseptoria tritici. The focus of this study was to predict the onset of the disease before the visual symptoms appeared on the plants. The results showed an early significant increase in the maximum temperature difference within the canopy from 1 to 7 days after inoculation (DAI). Biotic stress associated with increasing level of disease can be seen in the increasing average canopy temperature (ACT) and maximum temperature difference (MTD) and decreasing canopy temperature depression (CTD). However, only MTD (p ≤ 0.01) and CTD (p ≤ 0.05) parameters were significantly related to the disease level and can be used to predict the onset of fungal infection on wheat. The potential of thermography as a non-invasive high throughput phenotyping technique for early fungal disease detection in wheat was evident in this study.
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Bhargav Reddy, Maram, and Dumpapenchala Vijayreddy. "The Recent Applications of Remote sensing in Agriculture-A Review." Journal of Agriculture Biotechnology & Applied Sciences 1, no. 2 (March 2, 2025): 28–35. https://doi.org/10.63143/jabaas.2120233.
Full textAbstract:
Remote sensing is becoming a crucial technology in current agricultural practices, with several uses and benefits for farmers, researchers, and policymakers. Crop monitoring and management are the principal applications of remote sensing in agriculture. Remote sensing allows for the rapid and precise diagnosis of crop health, growth and yield estimation by evaluating data received from satellites or airborne platforms. This data assists farmers in optimising irrigation, fertilization, pest and disease control measures, resulting in better resource allocation, enhanced productivity and lower environmental consequences. The identification and mapping of crop diseases and pests is a key application. Remote sensing may detect minute differences in plant physiology, such as chlorophyll content changes, which may signal the presence of diseases or pest infestations. Initial identification allows for focused treatments such as precision pesticide application, disease avoidance and crop loss reduction. Precision agriculture relies heavily on remote sensing. Farmers may produce precise field maps that delineate differences in soil qualities, nutrient levels, and moisture content by integrating satellite photography, GPS navigation systems and computer algorithms. This data enables site-specific management, allowing farmers to deploy resources precisely where they are required, optimising inputs, lowering costs and minimising environmental consequences. Remote sensing makes land-use planning and monitoring easier. It can assist in identifying potential agricultural sites, assessing land degradation and tracking changes in land cover and land use trends over time. Policymakers can use this data to make informed decisions about land management, sustainable agriculture practices and conservation activities. It helps with agricultural water resource management. It is feasible to monitor water availability, assess irrigation demands and identify locations vulnerable to drought or water stress by studying satellite data. This information allows for more efficient water distribution, reducing water waste and improving water-use efficiency in agricultural activities. Remote sensing has numerous uses in agriculture, revolutionizing old farming practices. Keywords: Artificial intelligence, precision agriculture, Remote sensing, Satellites, Spectral reflectance, Sustainability
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Bhargav Reddy, Maram, and Dumpapenchala Vijay Reddy. "The Recent Applications of Remote sensing in Agriculture-A Review." Journal of Agriculture Biotechnology & Applied Sciences 1, no. 1 (March 2, 2025): 28–35. https://doi.org/10.63143/jabaas20232323.
Full textAbstract:
Remote sensing is becoming a crucial technology in current agricultural practices, with several uses and benefits for farmers, researchers and policymakers. Crop monitoring and management are the principal applications of remote sensing in agriculture. Remote sensing allows for the rapid and precise diagnosis of crop health, growth and yield estimation by evaluating data received from satellites or airborne platforms. This data assists farmers in optimising irrigation, fertilization, pest and disease control measures, resulting in better resource allocation, enhanced productivity and lower environmental consequences. The identification and mapping of crop diseases and pests is a key application. Remote sensing may detect minute differences in plant physiology, such as chlorophyll content changes, which may signal the presence of diseases or pest infestations. Initial identification allows for focused treatments such as precision pesticide application, disease avoidance and crop loss reduction. Precision agriculture relies heavily on remote sensing. Farmers may produce precise field maps that delineate differences in soil qualities, nutrient levels, and moisture content by integrating satellite photography, GPS navigation systems and computer algorithms. This data enables site-specific management, allowing farmers to deploy resources precisely where they are required, optimising inputs, lowering costs and minimising environmental consequences. Remote sensing makes land-use planning and monitoring easier. It can assist in identifying potential agricultural sites, assessing land degradation and tracking changes in land cover and land use trends over time. Policymakers can use this data to make informed decisions about land management, sustainable agriculture practices and conservation activities. It helps with agricultural water resource management. It is feasible to monitor water availability, assess irrigation demands and identify locations vulnerable to drought or water stress by studying satellite data. This information allows for more efficient water distribution, reducing water waste and improving water-use efficiency in agricultural activities. Remote sensing has numerous uses in agriculture, revolutionizing old farming practices. Keywords: Artificial intelligence, precision agriculture, remote sensing, satellites, spectral reflectance, sustainability
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Turechek, W. W., M. A. Ellis, and L. V. Madden. "Sequential Sampling for Incidence of Phomopsis Leaf Blight of Strawberry." Phytopathology® 91, no. 4 (April 2001): 336–47. http://dx.doi.org/10.1094/phyto.2001.91.4.336.
Full textAbstract:
Sequential sampling models for estimation and classification were developed for the incidence of strawberry leaflets infected by Phomopsis obscurans. Sampling protocols were based on a binary power law analysis of the spatial heterogeneity of Phomopsis leaf blight in commercial fields in Ohio. For sequential estimation, samples were collected until mean disease incidence could be estimated with a preselected coefficient of variation of the mean (C). For sequential classification, samples were collected until there was sufficient evidence to classify mean incidence as being below or above a threshold (pt) based on the sequential probability ratio test. Monte-Carlo simulations were used to determine the theoretical average sample number (ASN) and probability of classifying mean incidence as less than pt (operating characteristic) for any true value of incidence. Estimation and classification sampling models were both tested with bootstrap simulations of randomly selected data sets and validated by data sets from another year that were not utilized in developing the models. In general, achieved (or calculated) C after sequentially sampling for estimation was close to the preselected C of 0.2, and mean incidence was estimated with little bias. Achieving a C of 0.1 with less than 75 sampling units (the nominal value for many original data sets) was more problematical, especially with true incidence less than 0.2. ASN for classification was only 9 to 18 at disease incidence values near pt, and approximately five or less at incidence values far from pt. Correct classification decisions were made in over 88% of the validation data sets. Results indicated that it is possible to estimate Phomopsis leaf blight with high precision and with high correct classification probabilities.