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Journal articles on the topic 'Detecting Crop'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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4

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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10

Xue, Lulu, Minfeng Xing, and Haitao Lyu. "Improved Early-Stage Maize Row Detection Using Unmanned Aerial Vehicle Imagery." ISPRS International Journal of Geo-Information 13, no. 11 (2024): 376. http://dx.doi.org/10.3390/ijgi13110376.

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Monitoring row centerlines during early growth stages is essential for effective production management. However, detection becomes more challenging due to weed interference and crop row intersection in images. This study proposed an enhanced Region of Interest (ROI)-based approach for detecting early-stage maize rows. It integrated a modified green vegetation index with a dual-threshold algorithm for background segmentation. The median filtering algorithm was also selected to effectively remove most noise points. Next, an improved ROI-based feature point extraction method was used to eliminate

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11

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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12

Mutalik, Sumanth, Rashi, Rahamathunnisa, Rimsha, and Ms Chandana. "Crop Disease Prediction Using Web Application." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 840–45. http://dx.doi.org/10.22214/ijraset.2024.61696.

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Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a

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13

Yang, Shi Feng, Long Xue, and Ji Min Zhao. "Detecting System of Crop Disease Stress Based on Acoustic Emission and Virtual Technology." Applied Mechanics and Materials 556-562 (May 2014): 3331–34. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3331.

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A system for detecting the conditions of crop disease stress by acoustic emission technology was studied and developed .The PCI-2 acoustic emission board and R15 acoustic emission sensor probes were chosen to construct the hardware detecting system, and the AEWIN software and virtual instrument technology were utilized to construct the software system, a real-time acquisition and detecting system for the information between acoustic emission and disease stress of crop was established. The results show that there are a certain physiological cycle laws in the acoustic emission of healthy crop, g

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Palmares, April Joy A., and Patrick D. Cerna. "Detecting Sugarcane Pests and Diseases Using CNNs for Precision Crop Detection and Management." International Journal of Computer Science and Mobile Computing 14, no. 2 (2025): 44–51. https://doi.org/10.47760/ijcsmc.2025.v14i02.005.

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Effective disease management is essential for sustaining sugarcane yield and quality, and traditional methods, such as visual inspection and chemical analysis, are often costly and time-consuming. This study proposes an innovative solution that leverages artificial intelligence (AI) through Convolutional Neural Networks (CNNs) for advanced crop detection and management in sugarcane farming. The AI precision system aims to automate the detection of sugarcane pests and diseases by analyzing collected imagery using machine learning algorithms. This system processes various image parameters, inclu

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Corrales, David Camilo. "Toward detecting crop diseases and pest by supervised learning." Ingenieria y Universidad 19, no. 1 (2015): 207. http://dx.doi.org/10.11144/javeriana.iyu19-1.tdcd.

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El cambio climático ha provocado amenazas a la producción agrícola; los cambios extremos de temperatura y humedad, y otros factores de estrés abióticos contribuyen a la aparición de enfermedades y plagas en los cultivos. En este sentido, recientes esfuerzos de investigación se han enfocado en la predicción de plagas y enfermedades en cultivos haciendo uso de algoritmos de aprendizaje supervisado. En este artículo es presentada una revisión bibliográfica de los algoritmos de aprendizaje supervisado más utilizados para la detección de plagas y enfermedades en cultivos como: maíz, arroz, café, ma

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Çelen Erdem, İpek, Ceren Ünek, Pınar Akkuş Süt, Özge Karabıyık Acar, Meral Yurtsever, and Fikrettin Şahin. "Combined approaches for detecting polypropylene microplastics in crop plants." Journal of Environmental Management 347 (December 2023): 119258. http://dx.doi.org/10.1016/j.jenvman.2023.119258.

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17

Jabir, Brahim, Loubna Rabhi, and Noureddine Falih. "RNN- and CNN-based weed detection for crop improvement: An overview." Foods and Raw Materials 9, no. 2 (2021): 387–96. http://dx.doi.org/10.21603/2308-4057-2021-2-387-396.

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Introduction. Deep learning is a modern technique for image processing and data analysis with promising results and great potential. Successfully applied in various fields, it has recently entered the field of agriculture to address such agricultural problems as disease identification, fruit/plant classification, fruit counting, pest identification, and weed detection. The latter was the subject of our work. Weeds are harmful plants that grow in crops, competing for things like sunlight and water and causing crop yield losses. Traditional data processing techniques have several limitations and

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H.R., Vishwanatha, Vishwanatha K.C., Arun Das S., and Kimoto Koichi. "CROP RAID ANALYSIS; CROP WISE: AT NAGARAHOLE FOREST BUFFER VILLAGES." Shanlax International Journal of Arts, Science and Humanities 6, S2 (2019): 107–13. https://doi.org/10.5281/zenodo.2632489.

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<em>Elephants entering into human habitat in search of food has become a serious issue along the Nagarahole forest fringe. Although many measures have been taken to control the menace of elephants, it still persists due to human mistakes. Especially elephants are highly fond of eating human food crops such as paddy sugarcane ragi maize, banana and so on, in other words all types of human consuming farm crops are elephant’s food. Apart from the strong liking towards these crops, the elephants have strong sense of detecting the types of crops and age of crops from far of distance. This sen

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Kateb, Faris A., Muhammad Mostafa Monowar, Md Abdul Hamid, Abu Quwsar Ohi, and Muhammad Firoz Mridha. "FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards." Agronomy 11, no. 12 (2021): 2440. http://dx.doi.org/10.3390/agronomy11122440.

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Computer vision is currently experiencing success in various domains due to the harnessing of deep learning strategies. In the case of precision agriculture, computer vision is being investigated for detecting fruits from orchards. However, such strategies limit too-high complexity computation that is impossible to embed in an automated device. Nevertheless, most investigation of fruit detection is limited to a single fruit, resulting in the necessity of a one-to-many object detection system. This paper introduces a generic detection mechanism named FruitDet, designed to be prominent for detec

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Hidayah, A. H. Nurul, Syafeeza Ahmad Radzi, Norazlina Abdul Razak, Wira Hidayat Mohd Saad, Y. C. Wong, and A. Azureen Naja. "Disease Detection of Solanaceous Crops Using Deep Learning for Robot Vision." Journal of Robotics and Control (JRC) 3, no. 6 (2022): 790–99. http://dx.doi.org/10.18196/jrc.v3i6.15948.

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Traditionally, the farmers manage the crops from the early growth stage until the mature harvest stage by manually identifying and monitoring plant diseases, nutrient deficiencies, controlled irrigation, and controlled fertilizers and pesticides. Even the farmers have difficulty detecting crop diseases using their naked eyes due to several similar crop diseases. Identifying the correct diseases is crucial since it can improve the quality and quantity of crop production. With the advent of Artificial Intelligence (AI) technology, all crop-managing tasks can be automated using a robot that mimic

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21

Diao, Chunyuan, and Geyang Li. "Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology." Remote Sensing 14, no. 9 (2022): 1957. http://dx.doi.org/10.3390/rs14091957.

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Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated

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Shang, Jiali, Jiangui Liu, Valentin Poncos, et al. "Detection of Crop Seeding and Harvest through Analysis of Time-Series Sentinel-1 Interferometric SAR Data." Remote Sensing 12, no. 10 (2020): 1551. http://dx.doi.org/10.3390/rs12101551.

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Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode

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Grace, Amelia, Vera Kalitina, Daria Romanova, and Artem Engel. "Methods for detection of pathogens of cereal crops." Информатика. Экономика. Управление - Informatics. Economics. Management 3, no. 4 (2024): 0418–46. https://doi.org/10.47813/2782-5280-2024-3-4-0418-0446.

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The article presents the structure of the cereal crop family and their main characteristics in accordance with APG-II, and considers existing types of pathogens affecting cereal crops. The main methods for detecting and identifying cereal crop pathogens that pose a threat to crop yields and food security are described, and their advantages and disadvantages are analyzed. The authors emphasize that no method can completely replace others, and an integrated approach combining several methods is recommended to improve the reliability of diagnostics. Such an integrated approach allows for more acc

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B. I. KARANDE, M. M. LUNAGARIA, K. I. PATEL, and VYAS PANDEY. "Model for detecting nitrogen deficiency in wheat crop using spectral indices." Journal of Agrometeorology 16, no. 1 (2022): 85–93. http://dx.doi.org/10.54386/jam.v16i1.1491.

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An experiment was conducted four years (2007-08 to 20010-11) during rabi at Anand, India to study nitrogen stress response of wheat (Triticum aestivum L.) on spectral signature using spectroradiometer (Model - UniSpec DC Dual Channel Spectrometer, PP System, USA). The experiment was carried out with two wheat cultivars viz., GW496 and Lok 1 and five levels of nitrogen 120, 90, 60, 30 and 0 kg ha-1. The NDVI (normalized difference vegetation index) which is an indicator of status of crop biomass was found higher at 45 to 65 DAS of the crop age. The same characteristics were also observed in cas

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Park, Jae Chul. "A Study on the Fully Controlled Crop Cultivation System for the Development of Medicinal Crop Cultivation Model." Applied Mechanics and Materials 411-414 (September 2013): 3254–57. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.3254.

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The fully controlled crop cultivation system suggested in this study is intended to develop the cultivation model for stable production of medicinal crops under enclosed environment not affected by natural settings. In this paper, the features of the fully controlled crop cultivation system for detecting optimum conditions of medicinal crop cultivation are explored and technical components to be considered upon developing the system are reviewed.

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Pratik, Jadhav, Kachave Vishwambhar, Mane Aakash, and Joshi Kavita. "Crop detection using satellite image processing." i-manager’s Journal on Image Processing 10, no. 2 (2023): 50. http://dx.doi.org/10.26634/jip.10.2.19800.

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This research paper explores the application of satellite image processing techniques for crop detection in the agricultural industry. The primary objective is to provide insights into how these techniques can enhance crop yield and reduce losses, thereby contributing to global food security. The analysis employs advanced technologies and analytical methods to process satellite images and extract valuable information pertaining to crop growth and health. The findings demonstrate that satellite image processing offers accurate and timely data on crop conditions, enabling farmers to make well-in

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Murad, Nafeesa Yousuf, Tariq Mahmood, Abdur Rahim Mohammad Forkan, Ahsan Morshed, Prem Prakash Jayaraman, and Muhammad Shoaib Siddiqui. "Weed Detection Using Deep Learning: A Systematic Literature Review." Sensors 23, no. 7 (2023): 3670. http://dx.doi.org/10.3390/s23073670.

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Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting

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Gulden, Murzabekova, Glazyrina Natalya, Nekessova Anargul, et al. "Using deep learning algorithms to classify crop diseases." International Journal of Electrical and Computer Engineering (IJECE), no. 6 (December 1, 2023): 6737–44. https://doi.org/10.11591/ijece.v13i6.pp6737-6744.

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The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in detecting pests and crop

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Choudhary, Shikha, and Bhawna Saxena. "Analysing Machine Learning based Approaches for Detecting Late Blight Disease in Potato Crop." JOURNAL OF INTERNATIONAL ACADEMY OF PHYSICAL SCIENCES 27, no. 03 (2023): 285–93. http://dx.doi.org/10.61294/jiaps2023.2738.

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Agriculture is a significant contributor in the world economy. With the drastic change in the geographical conditions, the occurrence of extreme events like floods, droughts, heat waves, etc. are increasing, thereby harming crop yield. Additionally, crop yield is adversely impacted by crop diseases causing significant losses towards food production. Protecting against losses incurred by crop diseases can aid in improving food security as well as strengthening the economy. Traditional methods of crop disease detection are time and labor-intensive, whereas the use of machine learning (ML) based

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You, Jie, Wei Liu, and Joonwhoan Lee. "A DNN-based semantic segmentation for detecting weed and crop." Computers and Electronics in Agriculture 178 (November 2020): 105750. http://dx.doi.org/10.1016/j.compag.2020.105750.

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Mohammad Arif Ali Usmani. "A Machine Learning-Based Framework for Detecting Crop Nutrients Deficiencies." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 1183–202. https://doi.org/10.52783/jisem.v10i42s.8405.

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Efficient nutrient management is critical to enhancing agricultural productivity while promoting sustainable practices. However, traditional methods for diagnosing nutrient deficiencies in crops such as soil testing and visual inspection are often costly, time-consuming, and limited in scalability. This study proposes a machine learning-based framework for the automated detection of crop nutrient deficiencies, focusing on the three essential macronutrients: Nitrogen (N), Phosphorus (P), and Potassium (K). Utilizing a dataset of 1,156 leaf images, the system extracts 26 colour, texture, and sha

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Dr. Vijay Kumar Garg, Sandhya N. dhage,. "Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 810–17. http://dx.doi.org/10.17762/turcomat.v12i5.1488.

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Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming too late for diseases to be cured. Lack of early detection of diseases causes the diseases to be spread in

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Jeon, Hongyoung, and Heping Zhu. "Investigation of Depth Camera Potentials for Variable-Rate Sprayers." Journal of the ASABE 66, no. 1 (2023): 115–26. http://dx.doi.org/10.13031/ja.15070.

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Highlights A commercial depth camera with a custom-designed graphical user interface was evaluated to detect tree canopy. Measurement variations under different indoor conditions were negligible for practical applications. Measurement errors ranged from 2.8% to 15.8%, which were acceptable for outdoor applications. Variation of crabapple canopy detection rate was less than 6% from sunrise to sunset. Abstract. To reduce crop protection product use and environmental impacts while maintaining application efficacy and convenience for applicators, an automatic variable rate sprayer coupled with a c

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Murzabekova, Gulden, Natalya Glazyrina, Anargul Nekessova, et al. "Using deep learning algorithms to classify crop diseases." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6737. http://dx.doi.org/10.11591/ijece.v13i6.pp6737-6744.

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<span lang="EN-US">The use of deep learning algorithms for the classification of crop diseases is one of the promising areas in agricultural technology. This is due to the need for rapid and accurate detection of plant diseases, which allows timely measures to be taken to treat them and prevent their spread. One of them is to increase productivity and maintain land quality through the timely detection of diseases and pests in agriculture and their elimination. Traditional classification methods in machine learning and algorithms in deep learning were compared to note the high accuracy in

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Diao, Zhihua, Shushuai Ma, Dongyan Zhang, et al. "Algorithm for Corn Crop Row Recognition during Different Growth Stages Based on ST-YOLOv8s Network." Agronomy 14, no. 7 (2024): 1466. http://dx.doi.org/10.3390/agronomy14071466.

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Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in th

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Alanazi, Rakan. "A YOLOv10-based Approach for Banana Leaf Disease Detection." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23522–26. https://doi.org/10.48084/etasr.11138.

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Leaf disease detection plays a crucial role in modern agricultural management, enabling early intervention to minimize crop losses. This paper explores the application of the YOLOv10 model for detecting and classifying banana leaf conditions with high accuracy. A publicly available dataset of 938 images was used, categorized into five classes, namely Black-Sigatoka, Healthy-Leaf, Panama-Disease, Potassium-Deficiency, and Yellow-Sigatoka. The model achieved a mean Average Precision (mAP@0.5) of 88.85%, a precision of 91.22%, and a recall of 85.06%, demonstrating strong detection capabilities. T

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Sharda, Shikha, Sumit Kumar, Randhir Singh, et al. "DETECTING YELLOW RUST OF WHEAT AT VILLAGE LEVEL USING SENTINEL-2 SATELLITE IMAGES." International Journal on Biological Sciences 15, no. 02 (2024): 96–101. https://doi.org/10.53390/ijbs.2024.15204.

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Yellow rust is a destructive disease that adversely impact the growth and production of wheat. Previous studies shown that the parts of Rupnagar district, the foothill district of Punjab, is the most severely affected area for yellow rust of wheat because climate conditions in this area are favourable for its growth. Therefore, a study was planned to demonstrate the potential of Sentinel-2 images in detecting the yellow rust of wheat at village level (Nangal Nikku and Dukli villages of Rupnagar District of Punjab). The time series Normalized Difference Vegetation Index (NDVI) values from 27 Ja

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Ahmed, Ahmed Abdelmoamen, and Gopireddy Harshavardhan Reddy. "A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning." AgriEngineering 3, no. 3 (2021): 478–93. http://dx.doi.org/10.3390/agriengineering3030032.

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Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at sc

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Cheng, Zekai, Rongqing Huang, Rong Qian, Wei Dong, Jingbo Zhu, and Meifang Liu. "A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks." Applied Sciences 12, no. 15 (2022): 7378. http://dx.doi.org/10.3390/app12157378.

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Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of the method is derived from a simplified version of YOLOv3, namely YOLOLite, and k-means++ is utilized to improve the generation process of the prior boxes. In addition, a lightweight sandglass block and coordinate attention are used to optimize the structure of residual bloc

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K.Pranathi, Sri Navya Y., Aishwarya M., Vyshnavi B., Bharathi M., and Aditya Sai Srinivas T. "Deep Diagnosis: Improved Plant Leaf Disease Detection Using Neural Networks." Advancement of Computer Technology and its Applications 8, no. 1 (2024): 19–27. https://doi.org/10.5281/zenodo.13902459.

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<em>In agriculture-dependent countries like India, crop diseases cause significant losses, often spreading rapidly and affecting production. Detecting these diseases early is essential for farmers to act promptly and protect their crops. However, detecting plant diseases at early stages is challenging due to mild symptoms. This research paper introduces an enhanced CNN-based MCC-ACNN model, optimized with fine-tuned hyperparameters and varying batch sizes, aimed at improving the accuracy of plant leaf disease classification. Early and accurate identification is vital for boosting agricultural

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Naikal1, Ms Priyanka Lalasaheb. "Smart Agriculture: Utilizing Image Processing for Real-Time Crop Disease Monitoring." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36145.

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The K-Means clustering technique is a widely recognized method for low-level image segmentation challenges, converging iteratively to refine partitioning decisions based on an initial user-specified cluster set that updates with each iteration. Initially, our approach identifies predominantly green-colored pixels in the image, which are then masked based on threshold values computed using Otsu's method. Subsequent masking eliminates any remaining predominantly green pixels. Additionally, pixels with zero values for red, green, and blue channels, as well as those along the boundaries of the inf

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Lajurkar, Manik R., Aniruddha N. Barve, S. J. Waghmare, et al. "Applications of Drone for Crop Disease Detection and Monitoring: A Review." Asian Research Journal of Agriculture 18, no. 1 (2025): 15–25. https://doi.org/10.9734/arja/2025/v18i1638.

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Crop diseases are one of the major threats to global food production. The different crop diseases result in significant yield losses, where their effective monitoring and accurate early identification techniques are considered crucial to ensure stable and reliable crop productivity and food security. Restricting and managing the disease's spread and lowering the cost of pesticides require effective plant pathogen monitoring and detection. If not used in the early stages of pathogenesis, traditional techniques such as molecular and serological methods—which are frequently employed for plant dis

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Yang, Yanjun, Bo Tao, Liang Liang, et al. "Detecting Recent Crop Phenology Dynamics in Corn and Soybean Cropping Systems of Kentucky." Remote Sensing 13, no. 9 (2021): 1615. http://dx.doi.org/10.3390/rs13091615.

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Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with c

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S, Swathi. "Plant Disease Detection Using Machine Learning." Journal of Computer Allied Intelligence 3, no. 1 (2025): 48–56. https://doi.org/10.69996/jcai.2025005.

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Agricultural productivity is significantly affected by crop pathogens and pests, with unpredictable climatic conditions intensifying the challenge. This growing threat to global food security highlights the need for efficient plant disorder detection methods. Traditionally, naked eye observation is used for identifying plant disorders, but it requires continuous expert presence and struggles with visually similar symptoms. Existing automated detection approaches often rely on simple background datasets like Plant Village, which may not perform well in real-world conditions with complex backgro

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Pinki Kumari, Anza, Rajeev Kumar, Archana Kumar, and Ravi Kant Singh. "Nanosensors for Monitoring and Detecting Nanoparticle Effects on Crops." Journal of Environmental Nanotechnology 14, no. 1 (2025): 37–51. https://doi.org/10.13074/jent.2025.03.2511272.

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Nanotechnology has revolutionized biosensing in agriculture, offering unprecedented capabilities to monitor and enhance crop growth, detect contaminants, and ensure food safety. This review explores the applications of nanosensors in numerous aspects of agriculture, ranging from crop management to environmental monitoring. The paper begins with an exploration of biosensors and nanosensors, elucidating their essential characteristics and diverse applications in detecting biomolecules, toxic materials, and disease markers. Catalytic electrochemical biosensors, carbon nanotubes (CNTs), graphene n

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Zhu, Ruixue, Fengqi Hao, and Dexin Ma. "Research on Polygon Pest-Infected Leaf Region Detection Based on YOLOv8." Agriculture 13, no. 12 (2023): 2253. http://dx.doi.org/10.3390/agriculture13122253.

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Object detection in deep learning provides a viable solution for detecting crop-pest-infected regions. However, existing rectangle-based object detection methods are insufficient to accurately detect the shape of pest-infected regions. In addition, the method based on instance segmentation has a weak ability to detect the pest-infected regions at the edge of the leaves, resulting in unsatisfactory detection results. To solve these problems, we constructed a new polygon annotation dataset called PolyCorn, designed specifically for detecting corn leaf pest-infected regions. This was made to addr

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Yu, Jialin, Arnold W. Schumann, Shaun M. Sharpe, Xuehan Li, and Nathan S. Boyd. "Detection of grassy weeds in bermudagrass with deep convolutional neural networks." Weed Science 68, no. 5 (2020): 545–52. http://dx.doi.org/10.1017/wsc.2020.46.

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AbstractSpot spraying POST herbicides is an effective approach to reduce herbicide input and weed control cost. Machine vision detection of grass or grass-like weeds in turfgrass systems is a challenging task due to the similarity in plant morphology. In this work, we explored the feasibility of using image classification with deep convolutional neural networks (DCNN), including AlexNet, GoogLeNet, and VGGNet, for detection of crabgrass species (Digitaria spp.), doveweed [Murdannia nudiflora (L.) Brenan], dallisgrass (Paspalum dilatatum Poir.), and tropical signalgrass [Urochloa distachya (L.)

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Shargunam, S., and G. Rajakumar. "Defect Identification and Classification of Tomato Leaf Using Convolutional Neural Network." Asian Journal of Electrical Sciences 10, no. 1 (2021): 14–19. http://dx.doi.org/10.51983/ajes-2021.10.1.2834.

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Tomatoes are the most commonly grown crop globally, and they are used in almost every kitchen. India holds second place in the production of tomatoes. Due to the various kinds of diseases, the quantity and quality of tomato crop go down. Identifying the diseases in the earlier stage is very important and will help the farmers save the crop. The first initial step is pre-processing, for the Canny edge detection method is used for detecting the edges in the tomato leaves. The classification of tomato leaves is to be carried out by extracting the features like color, shape, and texture. Extracted

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Prof. Barry Wiling. "Monitoring of Sona Massori Paddy Crop and its Pests Using Image Processing." International Journal of New Practices in Management and Engineering 6, no. 02 (2017): 01–06. http://dx.doi.org/10.17762/ijnpme.v6i02.54.

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Nowadays plant diseases are the major cause of low agriculture yield. So significance of detecting diseases in early stages and treating it will improve the agriculture yield. In India the major agriculture crop is paddy and in central part of south India there is a specific paddy crop called Sona Massori. In our work we concentrated on Sona Masori paddy crop health and pest monitoring using image processing. Here image processing technique is used to observe the image of the leaf and based on the image the diseases are identified using the following process such as image acquisition, pre-proc

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Mekhalfi, Mohamed Lamine, Carlo Nicolò, Yakoub Bazi, Mohamad Mahmoud Al Rahhal, and Eslam Al Maghayreh. "Detecting Crop Circles in Google Earth Images with Mask R-CNN and YOLOv3." Applied Sciences 11, no. 5 (2021): 2238. http://dx.doi.org/10.3390/app11052238.

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Automatic detection and counting of crop circles in the desert can be of great use for large-scale farming as it enables easy and timely management of the farming land. However, so far, the literature remains short of relevant contributions in this regard. This letter frames the crop circles detection problem within a deep learning framework. In particular, accounting for their outstanding performance in object detection, we investigate the use of Mask R-CNN (Region Based Convolutional Neural Networks) as well as YOLOv3 (You Only Look Once) models for crop circle detection in the desert. In or

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