Relevant bibliographies by topics / Detecting Crop

Contents

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

Academic literature on the topic 'Detecting Crop'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Detecting Crop"

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Aravind, B. "Green Symphony: Deep Learning for Crop Health Assessment." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 199–206. https://doi.org/10.22214/ijraset.2025.71974.

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Abstract: The agricultural sector holds paramount importance in our economy, impacting our daily lives significantly. Effective management of agricultural resources is crucial for ensuring profitability in crop production. However, farmers often lack expertise in identifying and managing plant leaf diseases, leading to reduced yields. Detecting and classifying leaf diseases is pivotal for maximizing agricultural productivity. Utilizing Convolutional Neural Networks (CNNs) offers a promising solution for automated leaf disease detection and classification. This research focuses on detecting dis
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Kobayashi, T., M. Inagaki, S. Hata, and M. Takai. "CROP-ROW DETECTING SYSTEM BY NEURAL NETWORK." Acta Horticulturae, no. 319 (October 1992): 647–52. http://dx.doi.org/10.17660/actahortic.1992.319.104.

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Liu, Haojie, Hong Sun, Bohui Mao, Minzan Li, Man Zhang, and Qin Zhang. "Development of a Crop Growth Detecting System." IFAC-PapersOnLine 49, no. 16 (2016): 138–42. http://dx.doi.org/10.1016/j.ifacol.2016.10.026.

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Chattopadhyay, Dipanwita, Y. Balachandra, Ashoka, P, et al. "Precision Agriculture Technologies for Early Detection of Crop Pests and Diseases." UTTAR PRADESH JOURNAL OF ZOOLOGY 45, no. 20 (2024): 328–42. http://dx.doi.org/10.56557/upjoz/2024/v45i204588.

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Crop pests and diseases pose significant challenges to agricultural productivity and food security worldwide. Traditional methods for detecting and managing these threats often rely on manual scouting and blanket pesticide applications, which can be labor-intensive, time-consuming, and environmentally harmful. Precision agriculture technologies offer promising solutions for early detection and targeted management of crop pests and diseases. This review article provides a comprehensive overview of the latest precision agriculture tools and techniques for monitoring crop health, detecting pests
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Pineda Medina, Dunia, Ileana Miranda Cabrera, Rolisbel Alfonso de la Cruz, Lizandra Guerra Arzuaga, Sandra Cuello Portal, and Monica Bianchini. "A Mobile App for Detecting Potato Crop Diseases." Journal of Imaging 10, no. 2 (2024): 47. http://dx.doi.org/10.3390/jimaging10020047.

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Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architec
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B, Sushma, and Syed Rabbith. "A Review on Machine and Deep Learning Approaches for Crop Disease Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem.spejss009.

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Crop diseases significantly affect agricultural productivity and farmer livelihoods. Reducing crop losses and advancing sustainable farming require early detection and response. Recent advancements in Machine Learning (ML) and Deep Learning (DL) have shown promising results in crop disease detection using image analysis. This paper presents a detailed review of ML-based techniques for detecting crop diseases, with a focus on Convolutional Neural Networks (CNNs), transfer learning models, Realtime deployment through web applications like Streamlet, and the shortcomings of the methods used thus
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Kothari, Nitin, Chandra Jain, Aashita Jain, Anshika Solanki, Darshana Sen, and Priya Kumawat. "AI APPLICATION FOR CROP MONITORING AND PREDICT CROP DISEASES & SOIL QUALITIES." International Journal of Technical Research & Science 9, Spl (2024): 27–35. http://dx.doi.org/10.30780/specialissue-iset-2024/034.

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Crop disease and soil health are critical factors influencing agricultural productivity and food security worldwide. Traditional methods for disease detection and soil analysis often involve time-consuming and labourintensive processes, leading to delays in response and management. Given the current trajectory of population growth, it is anticipated that by 2050, global crop productivity will need to double from its current levels. Pests and diseases are a major obstacle to achieving this productivity outcome. Hence, it's imperative to devise efficient methodologies for automatically detecting
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Mehra, Ms Ritika. "Innovative Farming for Early Crop Disease Detection Using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1164–68. https://doi.org/10.22214/ijraset.2025.67378.

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Crop diseases are a major threat to global food security, causing significant losses in agricultural productivity. Traditional disease detection methods often rely on manual inspections, which can be time-consuming and prone to human error. Artificial Intelligence (AI) has emerged as a revolutionary tool in agriculture, offering accurate, efficient, and scalable solutions for detecting crop diseases. This paper explores the application of AI in innovative farming for crop disease detection, highlighting its methodologies, benefits, challenges, and future potential. Specific AI-driven applicati
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Aryan, Chaudhary, Gupta Mohit, and Tiwari Upasana. "Crop Disease Detection Using Deep Learning Models." Crop Disease Detection Using Deep Learning Models 8, no. 12 (2023): 9. https://doi.org/10.5281/zenodo.10432632.

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Detecting plant diseases during the growth of plants is a critical challenge in agriculture, as late detection can lead to reduced crop yields and lower profits for farmers. To tackle this issue, researchers have developed advanced frameworks based on Neural Networks[1]. However, many of these methods suffer from limited prediction accuracy or require a vast number of input variables. This project comprises of CNN and LSTM models, the CNN component of the project has demonstrated remarkable accuracy, achieving a 98.4% success rate in identifying plant diseases from static images. Keywords:- CN
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Wang, Heng, Xiangjie Qian, Lan Zhang, et al. "Detecting crop population growth using chlorophyll fluorescence imaging." Applied Optics 56, no. 35 (2017): 9762. http://dx.doi.org/10.1364/ao.56.009762.

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Dissertations / Theses on the topic "Detecting Crop"

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Owusu, Alex B. "Detecting and quantifying the extent of desertification and its impact in the semi-arid Sub-Saharan Africa a case study of the Upper East Region, Ghana /." Fairfax, VA : George Mason University, 2009. http://hdl.handle.net/1920/4576.

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Thesis (Ph.D.)--George Mason University, 2009.<br>Vita: p. 287. Thesis co-directors: Sheryl L. Beach, Guido Cervone. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Earth Systems and Geoinformation Sciences. Title from PDF t.p. (viewed Oct. 11, 2009). Includes bibliographical references (p. 267-286). Also issued in print.
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Chaaro, Lina, and Antón Laura Martínez. "Crop and weed detection using image processing and deep learning techniques." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18630.

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Artificial intelligence, specifically deep learning, is a fast-growing research field today. One of its various applications is object recognition, making use of computer vision. The combination of these two technologies leads to the purpose of this thesis. In this project, a system for the identification of different crops and weeds has been developed as an alternative to the system present on the FarmBot company’s robots. This is done by accessing the images through the FarmBot API, using computer vision for image processing, and artificial intelligence for the application of transfer learni
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Potiris, Steven. "Robust and Efficient Individual Plant Mapping of Vegetable Crops." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23251.

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Crop mapping can provide phenotype data for breeding research. To provide phenotyping data for individual plants, a system must be able to detect and segment each plant from its background. Many existing datasets reflect non-commercial cases where the plants do not overlap with weeds or other plants. The relative ease of segmenting the crops from background in these environments warrants the use of implicit instance segmentation models, which fail in more complex environments. This thesis investigates the use of explicit instance segmentation models to overcome the shortcomings of implicit m
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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<br>Department of Computing and Information Sciences<br>William H. Hsu<br>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 algorit
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Whitehurst, Daniel Scott. "Techniques for Processing Airborne Imagery for Multimodal Crop Health Monitoring and Early Insect Detection." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73048.

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During their growth, crops may experience a variety of health issues, which often lead to a reduction in crop yield. In order to avoid financial loss and sustain crop survival, it is imperative for farmers to detect and treat crop health issues. Interest in the use of unmanned aerial vehicles (UAVs) for precision agriculture has continued to grow as the cost of these platforms and sensing payloads has decreased. The increase in availability of this technology may enable farmers to scout their fields and react to issues more quickly and inexpensively than current satellite and other airborne me
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Karimi-Zindashty, Yousef. "Application of hyperspectral remote sensing in stress detection and crop growth modeling in corn fields." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85560.

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This study used hyperspectral data to determine nitrogen, weed, and water stresses in a corn (Zea mays L.) field in southwestern Quebec, and incorporated these data in crop growth models for better crop growth simulation under stressful conditions.<br>In 2000, aerial hyperspectral images (72 wavebands, ranging from 407 to 949 nm) were acquired, and analyzed using a stepwise approach to identify wavebands useful in detecting weed and nitrogen stresses. Discriminant analysis (DA) was used to classify different weed and nitrogen treatments and their combinations. This analysis showed great
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Budge, Giles Elliott. "Detection and characterisation of Rhizoctonia solani affecting UK Brassica crops." Thesis, University of Reading, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486318.

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Rhizoctonia so/ani is the causative agent of wirestem on Brassica crops. R. so/ani is a species complex comprising genetically. distinct groups known as anastomosis groups (AGs). Knowledge of which AGs are responsible for disease is necessary to formulate appropriate management strategies. Important knowledge about the specific AGs responsible for disease in UK Brassica crops and where in the production chain the fungus became associated with plants was lacking. A survey of UK .f3rassica o/eracea crops was completed to. establish which anastomosis groups of Rhizoctonia so/ani were associated w
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Crane, Andrew John. "The spectral detection of salt stress in cotton." Thesis, University of Portsmouth, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292358.

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Hughes, J. d'A. "Viruses of the Araceae and Dioscorea species : Their isolation, characterization and detection." Thesis, University of Reading, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375063.

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Kautz, Burkard [Verfasser]. "Fluorescence-based systems for detection of abiotic stresses on horticultural crops / Burkard Kautz." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1107541867/34.

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Books on the topic "Detecting Crop"

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1931-, Plucknett Donald L., and Sprague Howard Bennett 1898-, eds. Detecting mineral nutrient deficiencies in tropical and temperate crops. Westview Press, 1989.

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Connolly, Sheila. A killer crop. Berkley Publishing Group, 2010.

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Chong, Khim Phin, Jedol Dayou, and Arnnyitte Alexander. Detection and Control of Ganoderma boninense in Oil Palm Crop. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54969-9.

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LONG, Prasad. Instant Insights Advances Detecting Fo : Instant Insights: Advances in Detecting and Forecasting Crop Pests and Diseases. Burleigh Dodds Science Publishing Limited, 2023.

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Reyes, Rafael E. Detective Crap the Crap Detective: Children Story. Independently Published, 2019.

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McCutcheon, George Barr. Anderson Crow, Detective. Independently Published, 2019.

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McCutcheon, George Barr. Anderson Crow, Detective. Independently Published, 2019.

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McCutcheon, George Barr. Anderson Crow, Detective. Independently Published, 2019.

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Barr, McCutcheon George. Anderson Crow, Detective. Independently Published, 2021.

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McCutcheon, George Barr. Anderson Crow: Detective. Independently Published, 2020.

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Book chapters on the topic "Detecting Crop"

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Das, Bappa, Gopal R. Mahajan, and Ronald Singh. "Hyperspectral Remote Sensing: Use in Detecting Abiotic Stresses in Agriculture." In Advances in Crop Environment Interaction. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1861-0_12.

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Vishunavat, Karuna, Kuppusami Prabakar, and Theerthagiri Anand. "Seed Health: Testing and Management." In Seed Science and Technology. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5888-5_14.

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AbstractHealthy seeds play an important role in growing a healthy crop. Seed health testing is performed by detecting the presence or absence of insect infestation and seed-borne diseases caused by fungi, bacteria, and viruses. The most detrimental effect of seed-borne pathogens is the contamination of previously disease-free areas and the spread of new diseases. Sowing contaminated or infected seeds not only spreads pathogens but can also reduce yields significantly by 15–90%. Some of the major seed-borne diseases affecting yield in cereals, oilseeds, legumes, and vegetables, particularly in the warm and humid conditions prevailing in the tropical and sub-tropical regions, are blast and brown spot of rice, white tip nematode and ear-cockle in wheat, bacterial leaf blight of rice, downy mildews, smuts, head mould, seedling rots, anthracnose, halo blight, and a number of viral diseases. Hence, detection of seed-borne pathogens, such as fungi (anthracnose, bunt, smut, galls, fungal blights), bacteria (bacterial blights, fruit rots, cankers), viruses (crinkle, mottle, mosaic), and nematodes (galls and white tip), which transmit through infected seed to the main crop, is an important step in the management strategies for seed-borne diseases. Thus, seed health testing forms an essential part of seed certification, phytosanitary certification, and quarantine programmes at national and international levels. Detection of seed-borne/transmitted pathogens is also vital in ensuring the health of the basic stock used for seed production and in maintaining the plant germplasm for future research and product development. Besides the precise and reproducible testing methods, appropriate practices during seed production and post-harvest handling, including seed treatment and storage, are important components of seed health management and sustainable crop protection.
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Saisaward, Chanarun, and Sarawut Ninsawat. "Evaluation MODIS and Sentinel-2 Data for Detecting Crop Residue Burned Area." In Springer Geography. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16217-6_11.

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Chowdhury, Md Tanvir, Md Jakir Hossain, Omar Rafat Adnan, et al. "Detecting Crop Pests and Diseases Through Deep Learning Techniques for Improved Yields." In Cyber Intelligence and Information Retrieval. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3594-5_39.

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Sahu, Chandan Kumar, Prabira Kumar Sethy, and Santi Kumari Behera. "Sensing Technology for Detecting Insects in a Paddy Crop Field Using Optical Sensor." In Information and Communication Technology for Sustainable Development. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3932-4_20.

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Murrell, T. Scott, and Dharma Pitchay. "Evaluating Plant Potassium Status." In Improving Potassium Recommendations for Agricultural Crops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59197-7_9.

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AbstractSeveral methods exist for evaluating plant nutritional status. Looking for visual deficiency symptoms is perhaps the simplest approach, but once symptoms appear, crop performance has already been compromised. Several other techniques have been developed. All of them require correlation studies to provide plant performance interpretations. Reflectance is a remote sensing technique that detects changes in light energy reflected by plant tissue. It has proven successful in detecting nutrient deficiencies but does not yet have the ability to discriminate among more than one deficiency. Chemical assays of leaf tissue, known as tissue tests, require destructive sampling but are the standard against which other assessments are compared. Sufficiency ranges provide concentrations of each nutrient that are considered adequate for crop growth and development. They consider nutrients in isolation. Other approaches have been developed to consider how the concentration of one nutrient in tissue impacts the concentrations of other nutrients. These approaches strive to develop guidelines for maintaining nutrient balance within the plant. All approaches require large data sets for interpretation.
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Munusamy, Umaiyal, and Siti Nor Akmar Abdullah. "Tools and Targeted Genes for Plant Disease Detection." In Crop Improvement. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65079-1_16.

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Catucci, Antonella, Alessia Tricomi, Laura De Vendictis, Savvas Rogotis, and Nikolaos Marianos. "Farm Weather Insurance Assessment." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_19.

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AbstractThe pilot aimed to develop services supporting both the risk and the damage assessment in the agro-insurance domain. It is based on the use of remotely sensed data, integrated with meteorological data, and adopts machine learning and artificial intelligence tools. Netherlands and Greece have been selected as pilot areas . In the Netherlands, the pilot was focused on potato crops for the identification of areas with higher risk, based on the historical analysis of heavy rains. In addition, it covered automated detection of potato parcels with anomalous behaviours (damage assessment) from satellite data, meteorological parameters and soil characteristics. In Greece, the pilot worked with 7 annual crops of high economic interest to the national agricultural sector. The crops have been modelled exploiting the last 3-year NDVI measurements to identify their deviations from the normal crop health behaviour for an early identification of affected parcels in case of adverse events. The models were successfully tested on a flooding event that occurred in 2019 in the Komotini region. Even though the proposed methodologies should be tested over larger areas and compared against a larger validation dataset, the results already now demonstrate how to reduce the operating costs of damage assessors through a more precise and automatic risk assessment. Additionally, the identification of parameters that most affect the crop yield could transform the insurance industry through index-based solutions allowing to dramatically cut costs.
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Narayanasamy, P. "Biological Management of Crop Diseases." In Phyllosphere Microbial Plant Pathogens: Detection and Crop Disease Management. CRC Press, 2024. http://dx.doi.org/10.1201/9781003456834-3.

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Narayanasamy, P. "Crop Disease Management through Chemicals." In Phyllosphere Microbial Plant Pathogens: Detection and Crop Disease Management. CRC Press, 2024. http://dx.doi.org/10.1201/9781003456834-4.

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Conference papers on the topic "Detecting Crop"

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K, Sudha, Soorya Prakash D, and Sanjay Vishnu M. "Detecting Crop Pathogens with Convolutional Neural Networks." In 2025 International Conference on Frontier Technologies and Solutions (ICFTS). IEEE, 2025. https://doi.org/10.1109/icfts62006.2025.11031732.

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Praveen, RVS, Mudit Mittal, Prasanta Parida, Rowsonara Begum, Yogendra Kumar, and Ginni Nijhawan. "Deep Learning Applications for Detecting Crop Diseases from Image Data." In 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). IEEE, 2025. https://doi.org/10.1109/icccit62592.2025.10928061.

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Kaur, Arpanpreet. "Detecting Bean Crop Health: A ResNet50-based Approach to Disease Classification." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841685.

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Peng, Yiran, and Muhammad Abrar. "Agricultural Sustainability: Detecting Crop Diseases using Deep Learning and Image Processing." In 2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA). IEEE, 2025. https://doi.org/10.1109/dipca65051.2025.11042707.

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Chauhan, Shanvi, and Komuravelly Sudheer Kumar. "AI-Driven Potato Disease Classification: ResNet50 for Detecting Late Blight in Crop Leaves." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11042051.

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Jenipher, V. Nisha, and S. Radhika. "Expression of Concern for: An Automated System for Detecting Rice Crop Disease using CNN Inception V3 Transfer Learning Algorithm." In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS). IEEE, 2022. http://dx.doi.org/10.1109/icais53314.2022.10703364.

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Lu, Lizhen, Yiqing Zhu, and Jingwen Gu. "Detecting the Impacts of Ongoing Russia-Ukraine Conflict on Crop Growth and Human Activity Using Multi-Modal Remote Sensing Time-Series Data." In 2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2024. http://dx.doi.org/10.1109/agro-geoinformatics262780.2024.10660807.

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Gao, Yifeng, Heping Peng, Xiaoling Li, and Afei Gao. "YOLOv8 Lightweight Crop Disease Detection." In 2024 3rd International Conference on Advanced Sensing, Intelligent Manufacturing (ASIM). IEEE, 2024. https://doi.org/10.1109/asim66270.2024.00012.

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S, Unnathi C., and Kalpana Devi S. "Sustainable Farming: Crop Prediction and Detection of Crop Deficiency using Various Algorithms." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10763179.

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Zhang, Zizhe, and Jianjun Yin. "Small crop detection based on improved YOLOv8 algorithm for robotic pollination." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050413.

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Reports on the topic "Detecting Crop"

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Herrera C., Lorena, Laura Villamizar R., and Juliana Gómez V. Development of an immunological technique for detecting granulovirus infection in Tuta absoluta larvae (Lepidoptera: Gelechiidae). Corporación Colombiana de Investigación Agropecuaria - AGROSAVIA, 2012. http://dx.doi.org/10.21930/agrosavia.poster.2012.12.

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Tuta absoluta (Meyrick, 1917) (Lepidoptera: Gelechiidae), known as tomato moth or tomato leafminer is a microlepidopter from Gelechiidae’s family, which is widely distributed on America, Europe, Africa and Asia and is considered the most important pest of this crop (Roditakis et al. 2010). Phthorimaea operculella granulovirus (PhopGV) has been used for controlling larvae of different moths from Gelechiidae’s family as Tecia solanivora and P. operculella in several countries of South America as Colombia, Brazil, Argentina and Peru, and probably can also be pathogenic for T. absoluta larvae. How
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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish betwe
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Miles, Gaines E., Yael Edan, F. Tom Turpin, et al. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were st
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Jordan, Ramon L., Abed Gera, Hei-Ti Hsu, Andre Franck, and Gad Loebenstein. Detection and Diagnosis of Virus Diseases of Pelargonium. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568793.bard.

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Pelargonium (Geranium) is the number one pot plant in many areas of the United States and Europe. Israel and the U.S. send to Europe rooted cuttings, foundation stocks and finished plants to supply a certain share of the market. Geraniums are propagated mainly vegetatively from cuttings. Consequently, viral diseases have been and remain a major threat to the production and quality of the crop. Among the viruses isolated from naturally infected geraniums, 11 are not specific to Pelargonium and occur in other crops while 6 other viruses seem to be limited to geranium. However, several of these v
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Gavin, Greg, Paul Inkenbrandt, Trevor Schlossnagle, and Rebecca Molinari. Groundwater of Pahvant Valley, Millard County, Utah. Utah Geological Survey, 2024. http://dx.doi.org/10.34191/ss-173.

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Pahvant Valley, located in Millard County, Utah, encompasses 1610 square miles and includes several small towns, agricultural districts, hot springs, and biologically important wetlands, all heavily reliant on groundwater. This study, conducted by the Utah Geological Survey during 2022 and 2023, aims to define Pahvant Valley’s water recharge and discharge estimates, characterize its primary hydrogeologic units, and describe groundwater recharge and discharge areas. The research includes the collection of groundwater and surface water samples to estimate flow paths, sources of recharge and disc
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, contro
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real tim
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Upadhyaya, Shrini K., Abraham Shaviv, Abraham Katzir, Itzhak Shmulevich, and David S. Slaughter. Development of A Real-Time, In-Situ Nitrate Sensor. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7586537.bard.

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Although nitrate fertilizers are critical for enhancing crop production, excess application of nitrate fertilizer can result in ground water contamination leading to the so called "nitrate problem". Health and environmental problems related to this "nitrate problem" have led to serious concerns in many parts of the world including the United States and Israel. These concerns have resulted in legislation limiting the amount of nitrate N in drinking water to 10mg/g. Development of a fast, reliable, nitrate sensor for in-situ application can be extremely useful in dynamic monitoring of environmen
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Pomeroy, Robert, and Ryan Simkovsky. Integrated Pest Management (IPM) for Early Detection Algal Crop Protection (Final Report). Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1862344.

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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, 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
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