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Добірка наукової літератури з теми "Crop disease estimation"

Автор: Grafiati

Опубліковано: 9 травня 2025

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Статті в журналах з теми "Crop disease estimation"

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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Більше джерел

Дисертації з теми "Crop disease estimation"

Maas, Bea. "Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yield." Doctoral thesis, 2013. http://hdl.handle.net/11858/00-1735-0000-0022-5E77-5.

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Книги з теми "Crop disease estimation"

Study on the estimation of seed, feed, and post-harvest wastage of foodgrain crops in Bangladesh. Dhaka, Bangladesh: Uniconsult International, 1991.

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Keshav, Satish, and Alexandra Kent. Chronic diarrhoea. Edited by Patrick Davey and David Sprigings. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199568741.003.0029.

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Four to five per cent of the Western population suffers from chronic diarrhoea (defined as the passage of >3 stools per day, for >4 weeks), with irritable bowel syndrome (IBS) being the commonest cause in 20–40-year-old patients. It is the commonest reason for referral to secondary care gastroenterology clinics. The list of possible causes of chronic diarrhoea is long but, in the absence of rectal bleeding, loss of weight, or abnormal blood tests, it is unlikely to be due to a serious illness. Laboratory investigations should include serum glucose, electrolytes, renal and liver tests, full blood count, thyroid tests, a coeliac antibody test, C-reactive protein (CRP) measurement to check for systemic inflammation, faecal fat and elastase estimation to check pancreatic exocrine function, faecal microscopy, and culture, although this is insensitive for giardiasis. In young patients with typical features of IBS, these laboratory investigations can be abbreviated to include only glucose, electrolytes, the coeliac antibody test, CRP measurement, and thyroid tests. Endoscopic examination of the large and small intestines is generally only required where there is a suspicion of coeliac disease, chronic giardiasis, microscopic colitis, inflammatory bowel disease, or colorectal cancer. A therapeutic trial of metronidazole for giardiasis is justified where this seems a likely diagnosis.

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Частини книг з теми "Crop disease estimation"

Wyawahare, Medha, Jyoti Madake, Agnibha Sarkar, Anish Parkhe, Archis Khuspe, and Tejas Gaikwad. "Crop-Weed Detection, Depth Estimation and Disease Diagnosis Using YOLO and Darknet for Agribot: A Precision Farming Robot." In Algorithms for Intelligent Systems, 57–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4626-6_5.

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Bado, Souleymane, Fatemeh Maghuly, Vitor Varzea, and Margit Laimer. "Mutagenesis of in vitro explants of Coffea spp. to induce fungal resistance." In Mutation breeding, genetic diversity and crop adaptation to climate change, 344–52. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0036.

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Abstract Coffee is one of the most valuable commodity tree crops worldwide. However, it suffers from several devastating diseases and pests, for example coffee leaf rust and coffee berry borer, whose impact is being amplified by changing climatic conditions. Development of new adapted varieties remains a laborious effort by conventional breeding due to the long juvenile period in tree crops. Plant cell/tissue culture represents the ultimate method to produce large amounts of true-to-type healthy plants and of explants for mutation breeding. In fact, mutation induction combined with in vitro cell/tissue culture techniques has proved to be effective for developing improved cultivars of perennial crops. Prior to mutation breeding, cell and tissue radiosensitivity tests to various mutagens need to be performed, so that optimal treatments can be applied for large population development. Thus, different in vitro explants (plantlet, leaf, callus, embryogenic callus, globular and torpedo stage embryos) of Coffea arabica and Coffea canephora were exposed to different gamma-ray doses (0, 10, 15, 20, 40, 60 and 80 Gy). After 9-21 weeks incubation, a radiosensitivity test was conducted on the different explants and LD50 doses corresponding to 50% of viability or survival of callus, embryogenic callus, globular and torpedo stage embryos and 50% growth reduction (GR50) of shoot were also determined. Callus explants showed a relatively high radio-resistance (LD30-LD50 50-100 Gy) in comparison with entire plantlets or embryos (LD30-GR50 8-46 Gy). Globular embryo development into plantlets and also leaf area of irradiated plantlets were more severely affected by irradiation than other explants. It was possible to confirm the relative radio-resistance of unicellular explants compared with multicellular explants. Estimation of optimal mutation induction dosage range for various in vitro explants is important for tree crops, especially for coffee improvement.

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Prajapati, Jigna Bhupendra, Akash Kumar, Jhilam Pramanik, Bhupendra G. Prajapati, and Kavita Saini. "Edge AI for Real-Time and Intelligent Agriculture." In Applying Drone Technologies and Robotics for Agricultural Sustainability, 215–44. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6413-7.ch014.

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Advancements of the last decade in edge computing, edge IoT, and edge artificial intelligence now allow for autonomous, efficient, and intelligent systems to be proposed for various industrial applications. Intelligence agricultural solutions allow farmers to achieve more with less while improving quality and providing a rapid go-to-market approach for produce. Using AI is an effective technique to detect any crop health concerns or nutrient inadequacies in the field. Plant diseases affect the food system, economy, and environment. This chapter covers intelligent agriculture & challenges in front of technology. It focuses AI application using machine learning, artificial neural network (ANN), and deep learning. The various AI applications in agriculture for land monitoring, crop and varietal selection, smart irrigation or automation of irrigation, monitoring of crop health, crop disease detection, predictive analytics, weed control, precision agriculture, harvesting, yield estimation and phenotyping, supply chain management, and food quality.

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Usha, S. Gandhimathi Alias. "Harnessing Environmental Intelligence to Enhance Crop Management by Leveraging Deep Learning Technique." In Advances in Environmental Engineering and Green Technologies, 106–23. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9975-7.ch006.

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This chapter aims to enhance crop management practices by harnessing environmental intelligence through the power of deep learning techniques. Efficient and sustainable crop management is crucial for meeting the increasing demand for agricultural products while minimizing environmental impact. In recent years, the integration of deep learning techniques with environmental data has shown great potential in improving crop management practices. The proposed approach involves training deep learning model Dense Net 121 to predict important crop management factors, including yield estimation, disease, and pest outbreaks. The models are trained using historical and real-time data, enabling them to adapt and respond to dynamic environmental conditions. By capturing complex patterns and interactions, deep learning models can provide valuable insights and recommendations to farmers, enabling them to optimize resource allocation, reduce input wastage, and improve overall crop productivity.

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Tripathi, Smrati, Prasen Jeet, Rijwan Khan, and Akhilesh Kumar Srivastava. "Conventional to Modern Agriculture Using Artificial Intelligence." In Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0, 142–61. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0782-3.ch009.

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Because of increased pollution and population, the agriculture system is being affected drastically. New technologies should be implemented in this sector. The author discussed irrigation techniques and patterns using artificial intelligence that can change our irrigation process and minimize water consumption. Artificial intelligence approaches for soil fertility properties, the connection between soil quality, effect of fertiliser on soil possession and crop fecundity, season's growth, modelling of some soil properties, and estimation of polluted soil are being discussed. One more essential factor that reduces crop growth, quality, and yield is called weeds. Different types of weed control applications using ANN and ANFIS model have been discussed. To minimize monetary losses for farmers, disease control becomes a necessary parameter for better crop production. Image-based strategies using machine learning and deep learning for exact location, order of the disease, for precise and accurate identification have been considered.

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Roy, Pankaj, Mrutyunjay Padhiary, Azmirul Hoque, Bhabashankar Sahu, Dipak Roy, and Kundan Kumar. "Machine Learning for Precision Agriculture and Crop Yield Optimization." In Advances in Computational Intelligence and Robotics, 189–234. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9208-9.ch007.

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The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways of addressing complex challenges related to modern farming. This chapter discusses the use of ML in precision agriculture, emphasizing its capacity to maximize crop output and improve agricultural practices. It studies the use of supervised, unsupervised, reinforcement, and deep learning methodologies to evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, and agricultural equipment. Principal applications include predictive modeling for agricultural yield estimation, pest and disease identification, soil health assessment, irrigation optimization, and precision fertilization. The chapter also examines the problems and limits related to the implementation of machine learning in agriculture, including data quality and farmer acceptance.

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Raju, K. Srujan, K. Suneetha, K. Reddy Madhavi, Kondra Pranitha, J. Avanija, and B. Narendra Kumar Rao. "Enhancing Smart Agriculture Applications Utilizing Deep Learning Models and Computer Vision Techniques." In Agriculture and Aquaculture Applications of Biosensors and Bioelectronics, 238–52. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2069-3.ch012.

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The integration of deep learning models and computer vision techniques has created new agricultural possibilities, transforming traditional farming practices into smart and efficient operations. These advanced technologies have enabled farmers to optimise resource utilisation, manage crops effectively, maximise yields, and make informed decisions, resulting in increased crop productivity. One of the main applications of deep learning models is the usage of convolutional neural networks (CNNs) for detecting plant disease. By training on a large dataset containing images of healthy and diseased plants, these models can identify and prevent the spread of diseases among crops, significantly reducing losses. The transfer learning approach involves adapting pre-trained models to agricultural datasets, and improves disease identification capabilities by applying knowledge gained from general image datasets. Deep learning-based models combined with computer vision techniques play a significant role in monitoring crop growth and estimating yields.

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Bashir, Muhammad Jawad, and Rafia Mumtaz. "A Review of Advances in Computer Vision, Multi/Hyperspectral Imaging, UAVs, and Agri-Bots." In Advances in Computational Intelligence and Robotics, 149–90. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6255-6.ch006.

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Agriculture is vital to economic growth, contributing 4% to global GDP and over 25% in some developing countries. Most farming practices are outdated, necessitating modernization for improved efficiency. Advances in deep learning, multi- and hyperspectral imagery (MHSI), UAVs, and agri-bots have revolutionized precision agriculture (PA). Computer vision (CV) techniques, enhanced by MHSI, have automated tasks like crop classification, disease monitoring, and biomass estimation. UAVs assist in field scouting, disease detection, and precision spraying, while agri-bots with IoT sensors facilitate real-time data-driven actions such as fruit picking and weed control. This chapter reviews the latest developments in CV, MHSI, UAVs, and agri-bots, examining current methods, challenges, datasets, and future applications in precision agriculture.

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Ghosh, Sukanta, Shubhanshu Arya, and Amar Singh. "Plant Disease Detection Using Machine Learning Approaches." In Advances in Medical Technologies and Clinical Practice, 122–30. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7188-0.ch009.

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Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

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Shahi, Tej Bahadur, Ram Bahadur Khadka, and Arjun Neupane. "Applicability of UAV in Crop Health Monitoring Using Machine Learning Techniques." In Applications of Machine Learning in UAV Networks, 246–62. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0578-2.ch010.

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Food demands are increasing globally. Various issues such as urbanization, climate change, and desertification increasingly favour crop pests and diseases that limit crop productivity. Elaborating and discussing the pragmatic knowledge and information on recent advances in tools and techniques for crop monitoring developed in recent decades might help agronomists make more informed decisions. This chapter discusses the progress and development of new techniques equipped with recent sensors and platforms such as drones that have revolutionized the way of understanding plant physiology and stresses. It begins with the introduction to various tools available for crop stress estimation, mainly based on optical imaging such as multispectral, thermal, and hyperspectral imaging. An overview of unmanned aerial vehicle (UAV) -based image processing pipeline is presented and shed light on the possible avenues of UAV-based remote sensing for crop health monitoring using machine learning approaches.

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Тези доповідей конференцій з теми "Crop disease estimation"

P, Raghul, Kavitha A, Daniel Madan Raja S, Rathiya R, Krithik C.S, and Pranesh K.R. "Multi-Task Learning for Tomato Crop Disease Detection and Severity Estimation using CNN Framework." In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024. http://dx.doi.org/10.1109/adics58448.2024.10533592.

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A, Nithiya, Navina N, Thoshitha D, Suvetha R, and Thirilosana J. "Precision Agriculture Advancements: A Comprehensive Integrated System for Disease Prediction And Crop Yield Estimation Using Image Analysis And Environmental Data." In 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA). IEEE, 2024. http://dx.doi.org/10.1109/aimla59606.2024.10531324.

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Шпанев, А. М. "THE USE OF UNMANNED AERIAL VEHICLES AT MONITORING OF PHYTOSANITARY STATE OF AGROBIOCENOSES." In МАТЕРИАЛЫ II Всероссийской научной конференции с международным участием «ПРИМЕНЕНИЕ СРЕДСТВ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ В СЕЛЬСКОМ ХОЗЯЙСТВЕ» Санкт-Петербург, 26–28 сентября 2018 г. Crossref, 2018. http://dx.doi.org/10.25695/agrophysica.2018.2.18892.

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Рассмотрены промежуточные результаты многолетних исследований по оценке фитосанитарного состояния агробиоценозов, проводимых при помощи беспилотных летательных аппаратов на полях Меньковского филиала Агрофизического НИИ. Изучение спектральных характеристик фитосанитарного состояния посевов и посадок с.-х. культур основывалось на использовании тестовых площадок для дистанционного мониторинга и постоянных учетных площадок для наземного мониторинга распространения и развития вредных организмов. При этом тестовые и постоянные учетные площадки выступали в качестве эталонов с известными параметрами фитосанитарного состояния посевов, на основе которых проводилась последующая дешифровка аэрофотоснимков. Приведены результаты дистанционной оценки засоренности агробиоценозов и пораженности культурных растений некоторыми заболеваниями The paper considers the intermediate results of long-term studies on the estimation of phytosanitary state of agrobiocenoses carried out with the help of unmanned aerial vehicles. The study of the spectral characteristics of the crops phytosanitary state and plantings of agricultural crops was based on the use of test sites for remote monitoring and permanent accounting sites for ground monitoring of the spread and development of harmful organisms. Test and permanent registration sites were used as standards with known parameters of phytosanitary state of crops, on the basis of which the subsequent decoding of aerial photographs has been carried out. The results of the remote estimation of the infestation of agrobiocenoses and the damage of cultivated plants to some diseases are presented

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Eneroth, Hanna, Hanna Karlsson Potter, and Elin Röös. Environmental impact of coffee, tea and cocoa – data collection for a consumer guide for plant-based foods. Department of Energy and Technology, Swedish University of Agricultural Sciences, 2022. http://dx.doi.org/10.54612/a.2n3m2d2pjl.

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In 2020, WWF launched a consumer guide on plant-based products targeting Swedish consumers. The development of the guide is described in a journal paper (Karlsson Potter & Röös, 2021) and the environmental impact of different plant based foods was published in a report (Karlsson Potter, Lundmark, & Röös, 2020). This report was prepared for WWF Sweden to provide scientific background information for complementing the consumer guide with information on coffee, tea and cocoa. This report includes quantitative estimations for several environmental categories (climate, land use, biodiversity and water use) of coffee (per L), tea (per L) and cocoa powder (per kg), building on the previously established methodology for the consumer guide. In addition, scenarios of consumption of coffee, tea and cocoa drink with milk/plant-based drinks and waste at household level, are presented. Tea, coffee and cacao beans have a lot in common. They are tropical perennial crops traditionally grown in the shade among other species, i.e. in agroforestry systems. Today, the production in intensive monocultures has negative impact on biodiversity. Re-introducing agroforestry practices may be part of the solution to improve biodiversity in these landscapes. Climate change will likely, due to changes in temperature, extreme weather events and increases in pests and disease, alter the areas where these crops can be grown in the future. A relatively high ratio of the global land used for coffee, tea and cocoa is certified according to sustainability standards, compared to other crops. Although research on the implications of voluntary standards on different outcomes is inconclusive, the literature supports that certifications have a role in incentivizing more sustainable farming. Coffee, tea and cocoa all contain caffeine and have a high content of bioactive compounds such as antioxidants, and they have all been associated with positive health outcomes. While there is a strong coffee culture in Sweden and coffee contributes substantially to the environmental impact of our diet, tea is a less consumed beverage. Cocoa powder is consumed as a beverage, but substantial amounts of our cocoa consumption is in the form of chocolate. Roasted ground coffee on the Swedish market had a climate impact of 4.0 kg CO2e per kg powder, while the climate impact of instant coffee powder was 11.5 kg CO2e per kg. Per litre, including the energy use for making the coffee, the total climate impact was estimated to 0.25 kg CO2e per L brewed coffee and 0.16 kg CO2e per L for instant coffee. Less green coffee beans are needed to produce the same amount of ready to drink coffee from instant coffee than from brewed coffee. Tea had a climate impact of approximately 6.3 kg CO2 e per kg dry leaves corresponding to an impact of 0.064 CO2e per L ready to drink tea. In the assessment of climate impact per cup, tea had the lowest impact with 0.013 kg CO2e, followed by black instant coffee (0.024 kg CO2e), black coffee (0.038 kg CO2e), and cocoa drink made with milk (0.33 kg CO2e). The climate impact of 1kg cocoa powder on the Swedish market was estimated to 2.8 kg CO2e. Adding milk to coffee or tea increases the climate impact substantially. The literature describes a high proportion of the total climate impact of coffee from the consumer stage due to the electricity used by the coffee machine. However, with the Nordic low-carbon energy mix, the brewing and heating of water and milk contributes to only a minor part of the climate impact of coffee. As in previous research, coffee also had a higher land use, water use and biodiversity impact than tea per L beverage. Another factor of interest at the consumer stage is the waste of prepared coffee. Waste of prepared coffee contributes to climate impact through the additional production costs and electricity for preparation, even though the latter was small in our calculations. The waste of coffee and tea at Summary household level is extensive and measures to reduce the amount of wasted coffee and tea could reduce the environmental impact of Swedish hot drink consumption. For the final evaluation of coffee and tea for the consumer guide, the boundary for the fruit and vegetable group was used. The functional unit for coffee and tea was 1 L prepared beverage without any added milk or sweetener. In the guide, the final evaluation of conventionally grown coffee is that it is ‘yellow’ (‘Consume sometimes’), and for organic produce, ‘light green’ (‘Please consume). The evaluation of conventionally grown tea is that it is ‘light green’, and for organic produce, ‘dark green’ (‘Preferably consume this’). For cocoa, the functional unit is 1 kg of cocoa powder and the boundary was taken from the protein group. The final evaluation of conventionally grown cocoa is that it is ‘orange’ (‘Be careful’), and for organically produced cocoa, ‘light green’.

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