Relevant bibliographies by topics / Crop row detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Crop row detection'

Author: Grafiati
Published: 16 February 2023

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Journal articles on the topic "Crop row detection"

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Jiang Guoquan, 姜国权, 柯杏 Ke Xing, 杜尚丰 Du Shangfeng, 张漫 Zhang Man, and 陈娇 Chen Jiao. "Crop Row Detection Based on Machine Vision." Acta Optica Sinica 29, no. 4 (2009): 1015–20. http://dx.doi.org/10.3788/aos20092904.1015.

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Vidović, Ivan, Robert Cupec, and Željko Hocenski. "Crop row detection by global energy minimization." Pattern Recognition 55 (July 2016): 68–86. http://dx.doi.org/10.1016/j.patcog.2016.01.013.

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Ronchetti, Giulia, Alice Mayer, Arianna Facchi, Bianca Ortuani, and Giovanna Sona. "Crop Row Detection through UAV Surveys to Optimize On-Farm Irrigation Management." Remote Sensing 12, no. 12 (June 18, 2020): 1967. http://dx.doi.org/10.3390/rs12121967.

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Climate change and competition among water users are increasingly leading to a reduction of water availability for irrigation; at the same time, traditionally non-irrigated crops require irrigation to achieve high quality standards. In the context of precision agriculture, particular attention is given to the optimization of on-farm irrigation management, based on the knowledge of within-field variability of crop and soil properties, to increase crop yield quality and ensure an efficient water use. Unmanned Aerial Vehicle (UAV) imagery is used in precision agriculture to monitor crop variability, but in the case of row-crops, image post-processing is required to separate crop rows from soil background and weeds. This study focuses on the crop row detection and extraction from images acquired through a UAV during the cropping season of 2018. Thresholding algorithms, classification algorithms, and Bayesian segmentation are tested and compared on three different crop types, namely grapevine, pear, and tomato, for analyzing the suitability of these methods with respect to the characteristics of each crop. The obtained results are promising, with overall accuracy greater than 90% and producer’s accuracy over 85% for the class “crop canopy”. The methods’ performances vary according to the crop types, input data, and parameters used. Some important outcomes can be pointed out from our study: NIR information does not give any particular added value, and RGB sensors should be preferred to identify crop rows; the presence of shadows in the inter-row distances may affect crop detection on vineyards. Finally, the best methodologies to be adopted for practical applications are discussed.
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Zhang, Shaolin, Qianglong Ma, Shangkun Cheng, Dong An, Zhenling Yang, Biao Ma, and Yang Yang. "Crop Row Detection in the Middle and Late Periods of Maize under Sheltering Based on Solid State LiDAR." Agriculture 12, no. 12 (November 25, 2022): 2011. http://dx.doi.org/10.3390/agriculture12122011.

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As the basic link of autonomous navigation in agriculture, crop row detection is vital to achieve accurate detection of crop rows for autonomous navigation. Machine vision algorithms are easily affected by factors such as changes in field lighting and weather conditions, and the majority of machine vision algorithms detect early periods of crops, but it is challenging to detect crop rows under high sheltering pressure in the middle and late periods. In this paper, a crop row detection algorithm based on LiDAR is proposed that is aimed at the middle and late crop periods, which has a good effect compared with the conventional machine vision algorithm. The algorithm proposed the following three steps: point cloud preprocessing, feature point extraction, and crop row centerline detection. Firstly, dividing the horizontal strips equally, the improved K-means algorithm and the prior information of the previous horizontal strip are utilized to obtain the candidate points of the current horizontal strip, then the candidate points information is used to filter and extract the feature points in accordance with the corresponding threshold, and finally, the least squares method is used to fit the crop row centerlines. The experimental results show that the algorithm can detect the centerlines of crop rows in the middle and late periods of maize under the high sheltering environment. In the middle period, the average correct extraction rate of maize row centerlines was 95.1%, and the average processing time was 0.181 s; in the late period, the average correct extraction rate of maize row centerlines was 87.3%, and the average processing time was 0.195 s. At the same time, it also demonstrates accuracy and superiority of the algorithm over the machine vision algorithm, which can provide a solid foundation for autonomous navigation in agriculture.
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Romeo, J., G. Pajares, M. Montalvo, J. M. Guerrero, M. Guijarro, and A. Ribeiro. "Crop Row Detection in Maize Fields Inspired on the Human Visual Perception." Scientific World Journal 2012 (2012): 1–10. http://dx.doi.org/10.1100/2012/484390.

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This paper proposes a new method, oriented to image real-time processing, for identifying crop rows in maize fields in the images. The vision system is designed to be installed onboard a mobile agricultural vehicle, that is, submitted to gyros, vibrations, and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of two main processes: image segmentation and crop row detection. The first one applies a threshold to separate green plants or pixels (crops and weeds) from the rest (soil, stones, and others). It is based on a fuzzy clustering process, which allows obtaining the threshold to be applied during the normal operation process. The crop row detection applies a method based on image perspective projection that searches for maximum accumulation of segmented green pixels along straight alignments. They determine the expected crop lines in the images. The method is robust enough to work under the above-mentioned undesired effects. It is favorably compared against the well-tested Hough transformation for line detection.
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Ji, Ronghua, and Lijun Qi. "Crop-row detection algorithm based on Random Hough Transformation." Mathematical and Computer Modelling 54, no. 3-4 (August 2011): 1016–20. http://dx.doi.org/10.1016/j.mcm.2010.11.030.

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Zhai, Zhiqiang, Zhongxiang Zhu, Yuefeng Du, Zhenghe Song, and Enrong Mao. "Multi-crop-row detection algorithm based on binocular vision." Biosystems Engineering 150 (October 2016): 89–103. http://dx.doi.org/10.1016/j.biosystemseng.2016.07.009.

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Chen, Pengfei, Xiao Ma, Fangyong Wang, and Jing Li. "A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images." Remote Sensing 13, no. 17 (September 5, 2021): 3526. http://dx.doi.org/10.3390/rs13173526.

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Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.
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Kennedy, HannahJoy, Steven A. Fennimore, David C. Slaughter, Thuy T. Nguyen, Vivian L. Vuong, Rekha Raja, and Richard F. Smith. "Crop signal markers facilitate crop detection and weed removal from lettuce and tomato by an intelligent cultivator." Weed Technology 34, no. 3 (November 14, 2019): 342–50. http://dx.doi.org/10.1017/wet.2019.120.

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AbstractIncreasing weed control costs and limited herbicide options threaten vegetable crop profitability. Traditional interrow mechanical cultivation is very effective at removing weeds between crop rows. However, weed control within the crop rows is necessary to establish the crop and prevent yield loss. Currently, many vegetable crops require hand weeding to remove weeds within the row that remain after traditional cultivation and herbicide use. Intelligent cultivators have come into commercial use to remove intrarow weeds and reduce cost of hand weeding. Intelligent cultivators currently on the market such as the Robovator, use pattern recognition to detect the crop row. These cultivators do not differentiate crops and weeds and do not work well among high weed populations. One approach to differentiate weeds is to place a machine-detectable mark or signal on the crop (i.e., the crop has the mark and the weed does not), thereby facilitating weed/crop differentiation. Lettuce and tomato plants were marked with labels and topical markers, then cultivated with an intelligent cultivator programmed to identify the markers. Results from field trials in marked tomato and lettuce found that the intelligent cultivator removed 90% more weeds from tomato and 66% more weeds from lettuce than standard cultivators without reducing yields. Accurate crop and weed differentiation described here resulted in a 45% to 48% reduction in hand-weeding time per hectare.
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Hassanein, M., M. Khedr, and N. El-Sheimy. "CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 4, 2019): 349–56. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-349-2019.

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<p><strong>Abstract.</strong> Precision Agriculture (PA) management systems are considered among the top ten revolutions in the agriculture industry during the last couple decades. Generally, the PA is a management system that aims to integrate different technologies as navigation and imagery systems to control the use of the agriculture industry inputs aiming to enhance the quality and quantity of its output, while preserving the surrounding environment from any harm that might be caused due to the use of these inputs. On the other hand, during the last decade, Unmanned Aerial Vehicles (UAVs) showed great potential to enhance the use of remote sensing and imagery sensors for different PA applications such as weed management, crop health monitoring, and crop row detection. UAV imagery systems are capable to fill the gap between aerial and terrestrial imagery systems and enhance the use of imagery systems and remote sensing for PA applications. One of the important PA applications that uses UAV imagery systems, and which drew lots of interest is the crop row detection, especially that such application is important for other applications such as weed detection and crop yield predication. This paper introduces a new crop row detection methodology using low-cost UAV RGB imagery system. The methodology has three main steps. First, the RGB images are converted into HSV color space and the Hue image are extracted. Then, different sections are generated with different orientation angles in the Hue images. For each section, using the PCA of the Hue values in the section, an analysis can be performed to evaluate the variances of the Hue values in the section. The crop row orientation angle is detected as the same orientation angle of the section that provides the minimum variances of Hue values. Finally, a scan line is generated over the Hue image with the same orientation angle of the crop rows. The scan line computes the average of the Hue values for each line in the Hue image similar to the detected crop row orientation. The generated values provide a graph full of peaks and valleys which represent the crop and soil rows. The proposed methodology was evaluated using different RGB images acquired by low-cost UAV for a Canola field. The images were taken at different flight heights and different dates. The achieved results proved the ability of the proposed methodology to detect the crop rows at different cases.</p>
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Dissertations / Theses on the topic "Crop row detection"

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Varshney, Varun. "Supervised and unsupervised learning for plant and crop row detection in precision agriculture." Thesis, Kansas State University, 2017. http://hdl.handle.net/2097/35463.

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Master of Science
Department of Computing and Information Sciences
William H. Hsu
The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop rows. Aerial images, taken by an Unmanned Aerial Vehicle (UAV), of a corn field at various stages of growth were acquired in RGB format through the Agronomy Department at the Kansas State University. Several segmentation and clustering approaches were applied to these images, namely K-Means clustering, Excessive Green (ExG) Index algorithm, Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and a deep learning approach based on Fully Convolutional Networks (FCN), to detect the plants present in the images. A Hough Transform (HT) approach was used to detect the orientation of the crop rows and rotate the images so that the rows became parallel to the x-axis. The result of applying different segmentation methods to the images was then used in estimating the location of crop rows in the images by using a template creation method based on Green Pixel Accumulation (GPA) that calculates the intensity profile of green pixels present in the images. Connected component analysis was then applied to find the centroids of the detected plants. Each centroid was associated with a crop row, and centroids lying outside the row templates were discarded as being weeds. A comparison between the various segmentation algorithms based on the Dice similarity index and average run-times is presented at the end of the work.
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Bah, Mamadou Dian. "Détection des adventices par imagerie aérienne." Thesis, Orléans, 2020. http://www.theses.fr/2020ORLE3190.

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Dans le contexte agricole actuel, il est nécessaire de réduire l’utilisation des produits phytosanitaires contre les mauvaises herbes. Le désherbage localisé présente une alternative prometteuse pour limiter les coûts et l’impact environnemental. Cependant, la localisation automatique des adventices n’est pas une tâche facile car elle présente plusieurs défis scientifiques et technologiques. L’objectif de cette thèse est de proposer des méthodes de traitement d’images et d’intelligence artificielle pour la localisation des adventices en grandes cultures. Dans ce cadre, nous avons abordé deux problématiques, la détection des rangées de culture et la détection des adventices. Deux méthodes ont été proposées pour la détection des rangées de culture. La première méthode combine la transformée de Hough et l’algorithme de regroupement linéaire itératif SLIC. La deuxième, quant à elle, utilise une approche totalement nouvelle basée sur l’apprentissage profond. Ces deux méthodes ont été utilisées pour détecter les adventices inter-rang et celles qui sont en contact avec les rangées de culture. Pour tendre vers une meilleur efficacité, deux nouvelles méthodes de détection d’adventices par apprentissage machine, entièrement automatiques ont été développées. L’originalité de ces méthodes est que l’apprentissage est effectué sur des données annotées automatiquement. La première méthode est basée sur l’apprentissage profond tandis que la seconde génère des modèles à partir de descripteurs profonds et un classifieur à classe unique. Les résultats obtenus sur des données réelles montrent l’intérêt des approches proposées
In the current agricultural context, there is a need to reduce the use of pesticides for weed control. Localized weed control presents a promising option to limit costs and environmental impact. However, automatic weed detection is not an easy task and presents several scientific and technological challenges. The objective of this thesis is to propose image processing and artificial intelligence methods for weed detection in field crops. Within this framework, we addressed two issues, crop row detection and weed detection. Two methods were proposed for crop row detection. The first method combines the Hough transform and the simple linear iterative clustering SLIC. The second one uses a completely new approach using deep learning. Both methods were used to detect inter-row weeds and weeds in contact with crop rows. To achieve greater efficiency, two new fully automatic machine learning weed detection methods have been developed. The originality of these methods is that learning is carried out on automatically annotated data. The first method is based on deep learning while the second method generates models from deep features and one-lass classifier. The results obtained on real data show the interest of the proposed approaches
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Gobor, Zoltan. "Development of a novel mechatronic system for mechanical weed control of the intra row area in row crops based in detection of single plants and adequate controlling of the hoeing tool in real time /." Bonn : Inst. für Landtechnik, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016668688&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Romberg, Megan Kara. "Research into two diseases of solanaceous crops in California : 1) characterization of potato early dying in Kern County, California. 2) phylogeny, host range and molecular detection of Fusarium solani f.sp. eumartii, causal agent of Eumartii wilt in potato, foot rot of tomato and stem rot of pepper /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2005. http://uclibs.org/PID/11984.

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Alshanbari, Reem. "Artificial-Intelligence-Enabled Robotic Navigation Using Crop Row Detection Based Multi-Sensory Plant Monitoring System Deployment." Thesis, 2021. http://hdl.handle.net/10754/670240.

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The ability to detect crop rows and release sensors in large areas to ensure homogeneous coverage is crucial to monitor and increase the yield of crop rows. Aerial robotics in the agriculture field helps to reduce soil compaction. We report a release mechanics system based on image processing for crop row detection, which is essential for field navigation-based machine vision since most plants grow in a row. The release mechanics system is fully automated using embedded hardware and operated from a UAV. Once the crop row is detected, the release mechanics system releases lightweight, flexible multi-sensory devices on top of each plant to monitor the humidity and temperature conditions. The capability to monitor the local environmental conditions of plants can have a high impact on enhancing the plant’s health and in creasing the output of agriculture. The proposed algorithm steps: image acquisition, image processing, and line detection. First, we select the Region of Interest (ROI) from the frame, transform it to grayscale, remove noise, and then skeletonize and remove the background. Next, apply a Hough transform to detect crop rows and filter the lines. Finally, we use the Kalman filter to predict the crop row line in the next frame to improve the performance. This work’s main contribution is the release mechanism integrated with embedded hardware with a high-performance crop row detection algorithm for field navigation. The experimental results show the algorithm’s performance achieved a high accuracy of 90% of images with resolutions of (900x470) the speed reached 2 Frames Per Second (FPS).
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Books on the topic "Crop row detection"

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Rowling, J. K. Harry Potter & chirec croc lyua. TP. Hso Chí Minh: NXB Trke, 2002.

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Rowling, J. K. Harry Potter & chirec croc lyua. 2nd ed. TP. Hso Chí Minh: NXB Trke, 2007.

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Book chapters on the topic "Crop row detection"

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Pusdá-Chulde, Marco, Armando De Giusti, Erick Herrera-Granda, and Iván García-Santillán. "Parallel CPU-Based Processing for Automatic Crop Row Detection in Corn Fields." In Artificial Intelligence, Computer and Software Engineering Advances, 239–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68080-0_18.

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Reiser, David, Garrido Miguel, Manuel Vázquez Arellano, Hans W. Griepentrog, and Dimitris S. Paraforos. "Crop Row Detection in Maize for Developing Navigation Algorithms Under Changing Plant Growth Stages." In Advances in Intelligent Systems and Computing, 371–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27146-0_29.

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Wu, Jian, Mengwei Deng, Lianlian Fu, and Jianqun Miao. "Vanishing Point Conducted Diffusion for Crop Rows Detection." In Advances in Intelligent, Interactive Systems and Applications, 404–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02804-6_54.

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Franzen, David W., Yuxin Miao, Newell R. Kitchen, James S. Schepers, and Peter C. Scharf. "Sensing for Health, Vigour and Disease Detection in Row and Grain Crops." In Sensing Approaches for Precision Agriculture, 159–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78431-7_6.

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García-Santillán, Iván, Diego Peluffo-Ordoñez, Víctor Caranqui, Marco Pusdá, Fernando Garrido, and Pedro Granda. "Computer Vision-Based Method for Automatic Detection of Crop Rows in Potato Fields." In Proceedings of the International Conference on Information Technology & Systems (ICITS 2018), 355–66. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73450-7_34.

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Kumari, Nidhi, and Shabnam Katoch. "Wilt and Root Rot Complex of Important Pulse Crops: Their Detection and Integrated Management." In Fungal Biology, 93–119. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35947-8_6.

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Thinggaard, K. "Screening Techniques for Detection of Resistance to Root Rot Caused by Phytophthora Spp. In Horticultural Crops." In Durability of Disease Resistance, 352. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2004-3_78.

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Luna-Santamaría, Javier, Jose Ramiro Martínez de Dios, and Anibal Ollero Baturone. "LIDAR-based detection of furrows for agricultural robot autonomous navigation." In XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), 728–34. 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.9788497498418.0728.

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Robust and accurate autonomous navigation is a main challenge in agricultural robotics. This paper presents a LIDAR-based processing system for autonomous robot navigation in crops with high vegetation density. The method detects and locates the crop furrows and provides them to the robot control system, which guides the robot such that its caterpillar tracks move along the furrows preventing damages in the crop. The proposed LIDAR-based processing pipeline includes various inconsistencies removal and template matching steps to deal with the high noise level of LIDAR scans. It has been implemented in C++ using ROS Noetic and validated in two different plantations with different crop growth status.
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Nand Tripathi, Atma, Shailesh Kumar Tiwari, and Tushar Kanti Behera. "Postharvest Diseases of Vegetable Crops and Their Management." In Postharvest Technology - Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101852.

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Vegetable crops have an important role in food and nutrition and maintain the health of soil. India is the second-largest producer of vegetables in the world with a 16% (191.77 MT) share of global vegetable production. Every year, diseases cause postharvest losses (40–60%) in vegetable crops due to their perishable nature under field (15–20%), packaging and storage (15–20%), and transport (30–40%). Profiling, detection, and diagnosis of postharvest vegetable pathogens (diseases) are essential for better understanding of pathogen and formulation of safe management of postharvest spoilage of vegetables. The vegetable produce is spoiled by postharvest pathogens and makes them unfit for human consumption and market due to the production of mycotoxins and other potential human health risks. Genera of fungal pathogens viz. Alternaria, Aschochyta, Colletotrichum, Didymella, Phoma, Phytophthora, Pythium, Rhizoctonia, Sclerotinia, Sclerotium, and bacterial pathogens viz. Erwinia spp., Pseudomonas spp., Ralstonia solanacearum, Xanthomonas euvesictoria were recorded as postharvest pathogens on vegetable crops. Fruit rot incidence of several post-harvest pathogens viz. Alternaria solani (30%), Phytophthora infestans (15%), Rhophitulus solani (30%), Sclerotium rolfsii (30%) fruit rot and X. euvesictoria (5%) canker on tomato; Colletotrichum dematium fruit rot (20%) on chili; Phomopsis vexans (60%) fruit rot on brinjal was recorded. Didymella black rot and Colletotrichum anthracnose were recorded on fruits of bottle gourd, pumpkin, ash gourd, and watermelon. Important leguminous vegetable crops are infected by postharvest pathogens viz. Ascochyta pisi, Colletotrichum lindemuthianum (Anthracnose), Sclerotinia sclerotiorum (white rot) and Pseudomonas syringae pv. phaseolicola (blight), Sclerotinia white rot, Alternaria blight. However, Xanthomonas black rot (10%) on cabbage and Pectinovora (Erwinia) soft rot (19%) were recorded as emerging post-harvest pathogens on cauliflower.
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Rajesh T. M., Kavyashree Dalawai, and Pradeep N. "Automatic Data Acquisition and Spot Disease Identification System in Plants Pathology Domain." In Modern Techniques for Agricultural Disease Management and Crop Yield Prediction, 111–41. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9632-5.ch006.

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Plants play one of the main roles in our ecosystem. Manual identification for the leaves sometimes leads to greater difference due to look alike. People often get confused with lookalike leaves which mostly end in loss of life. Authentication of original leaf with look-alike leaf is very essential nowadays. Disease identification of plants are proved to be beneficial for agro-industries, research, and eco-system balancing. In the era of industrialization, vegetation is shrinking. Early detection of diseases from the dataset of leaf can be rewarding and help in making our environment healthier and green. Implementation involves proper data acquisition where pre-processing of images is done for error correction if present in the raw dataset. It is followed by feature extraction stage to get the best results in further classification stage. K-mean, PCA, and ICA algorithms are used for identification and clustering of diseases in plants. The implementation proves that the proposed method shows promising result on the basis of histogram of gradient (HoG) features.
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Conference papers on the topic "Crop row detection"

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Khan, Nazmuzzaman, Veera P. Rajendran, Mohammad Al Hasan, and Sohel Anwar. "Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23950.

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Abstract Autonomous navigation of agricultural robot is an essential task in precision agriculture, and success of this task critically depends on accurate detection of crop rows using computer vision methodologies. This is a challenging task due to substantial natural variations in crop row images due to various factors, including, missing crops in parts of a row, high and irregular weed growth between rows, different crop growth stages, different inter-crop spacing, variation in weather condition, and lighting. The processing time of the detection algorithm also needs to be small so that the desired number of image frames from continuous video can be processed in real-time. To cope with all the above mentioned requirements, we propose a crop row detection algorithm consisting of the following three linked stages: (1) color based segmentation for differentiating crop and weed from background, (2) differentiating crop and weed pixels using clustering algorithm and (3) robust line fitting to detect crop rows. We test the proposed algorithm over a wide variety of scenarios and compare its performance against four different types of existing strategies for crop row detection. Experimental results show that the proposed algorithm perform better than the competing algorithms with reasonable accuracy. We also perform additional experiment to test the robustness of the proposed algorithm over different values of the tuning parameters and over different clustering methods, such as, KMeans, MeanShift, Agglomerative, and HDBSCAN.
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Doha, Rashed, Mohammad Al Hasan, and Sohel Anwar. "Semantic Segmentation Approaches in Crop Row Detection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021091.

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Zheng, Li-Ying, and Jing-Xue Xu. "Multi-crop-row detection based on strip analysis." In 2014 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2014. http://dx.doi.org/10.1109/icmlc.2014.7009678.

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Martinez-Vargas, Anabel, Julio C. Ramos-Fernández, Gerardo Salvador Romo-Cárdenas, Gener Aviles-Rodriguez, and Maria Cosio-Leon. "Crop row detection a bioinspired and data analysis approach." In Applications of Digital Image Processing XLI, edited by Andrew G. Tescher. SPIE, 2018. http://dx.doi.org/10.1117/12.2319238.

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Lei Zhang and Tony E Grift. "A New Approach to Crop-Row Detection in Corn." In 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.29834.

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Jinlin, Xue, and Ju Weiping. "Vision-Based Guidance Line Detection in Row Crop Fields." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.400.

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Tu, Chunling, Barend Jacobus van Wyk, Karim Djouani, Yskandar Hamam, and Shengzhi Du. "An efficient crop row detection method for agriculture robots." In 2014 7th International Congress on Image and Signal Processing (CISP). IEEE, 2014. http://dx.doi.org/10.1109/cisp.2014.7003860.

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Doha, Rashed, Mohammad Al Hasan, Sohel Anwar, and Veera Rajendran. "Deep Learning based Crop Row Detection with Online Domain Adaptation." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467155.

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de Silva, Rajitha, Grzegorz Cielniak, and Junfeng Gao. "Towards Infield Navigation: leveraging simulated data for crop row detection." In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 2022. http://dx.doi.org/10.1109/case49997.2022.9926670.

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Ota, Kumpei, Jun Younes Louhi Kasahara, Atsushi Yamashita, and Hajime Asama. "Weed and Crop Detection by Combining Crop Row Detection and K-means Clustering in Weed Infested Agricultural Fields." In 2022 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2022. http://dx.doi.org/10.1109/sii52469.2022.9708815.

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Reports on the topic "Crop row detection"

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Sessa, Guido, and Gregory Martin. role of FLS3 and BSK830 in pattern-triggered immunity in tomato. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604270.bard.

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Pattern-recognition receptors (PRRs) located on the plant cell surface initiate immune responses by perceiving conserved pathogen molecules known as pathogen-associated molecular patterns (PAMPs). PRRs typically function in multiprotein complexes that include transmembrane and cytoplasmickinases and contribute to the initiation and signaling of pattern-triggered immunity (PTI). An important challenge is to identify molecular components of PRR complexes and downstream signaling pathways, and to understand the molecular mechanisms that mediate their function. In research activities supported by BARD-4931, we studied the role of the FLAGELLIN SENSING 3 (FLS3) PRR in the response of tomato leaves to flagellin-derivedPAMPs and PTI. In addition, we investigated molecular properties of the tomato brassinosteroid signaling kinase 830 (BSK830) that physically interacts with FLS3 and is a candidate for acting in the FLS3 signaling pathway. Our investigation refers to the proposal original objectives that were to: 1) Investigate the role of FLS3 and its interacting proteins in PTI; 2) Investigate the role of BSK830 in PTI; 3) Examine molecular and phosphorylation dynamics of the FLS3-BSK830 interaction; 4) Examine the possible interaction of FLS3 and BSK830 with Pstand Xcveffectors. We used CRISPR/Cas9 techniques to develop plants carrying single or combined mutations in the FLS3 gene and in the paralogsFLS2.1 and FLS2.2 genes, which encode the receptor FLAGELLIN SENSING2 (FLS2), and analyzed their function in PTI. Domain swapping analysis of the FLS2 and FLS3 receptors revealed domains of the proteins responsible for PAMP detection and for the different ROS response initiated by flgII-28/FLS3 as compared to flg22/FLS2. In addition, in vitro kinase assays and point mutations analysis identified FLS2 and FLS3 domains required for kinase activity and ATP binding. In research activities on tomato BSK830, we found that it interacts with PRRs and with the co-receptor SERK3A and PAMP treatment affects part of these interactions. CRISPR/Cas9 bsk830 mutant plants displayed enhanced pathogen susceptibility and reduced ROS production upon PAMP treatment. In addition, BSK830 interacted with 8 Xanthomonastype III secreted effectors. Follow up analysis revealed that among these effectors XopAE is part of an operon, is translocated into plant cells, and displays E3 ubiquitinligase activity. Our investigation was also extended to other Arabidopsis and tomato BSK family members. Arabidopsis BSK5 localized to the plant cell periphery, interacted with receptor-like kinases, and it was phosphorylatedin vitro by the PEPR1 and EFRPRRs. bsk5 mutant plants displayed enhanced susceptibility to pathogens and were impaired in several, but not all, PAMP-induced responses. Conversely, BSK5 overexpression conferred enhanced disease resistance and caused stronger PTI responses. Genetic complementation suggested that proper localization, kinase activity, and phosphorylation by PRRs are critical for BSK5 function. BSK7 and BSK8 specifically interacted with the FLS2 PRR, their respective mutant plants were more susceptible to B. cinereaand displayed reduced flg22-induced responses. The tomato BSK Mai1 was found to interact with the M3KMAPKKK, which is involved in activation of cell death associated with effector-triggered immunity. Silencing of Mai1 in N. benthamianaplants compromised cell death induced by a specific class of immune receptors. In addition, co-expression of Mai1 and M3Kin leaves enhanced MAPKphosphorylation and cell death, suggesting that Mai1 acts as a molecular link between pathogen recognition and MAPK signaling. Finally, We identified the PP2C phosphatase Pic1 that acts as a negative regulator of PTI by interacting with and dephosphorylating the receptor-like cytoplasmickinase Pti1, which is a positive regulator of plant immunity. The results of this investigation shed new light on the molecular characteristics and interactions of components of the immune system of crop plants providing new knowledge and tools for development of novel strategies for disease control.
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