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Articles de revues sur le sujet « Pest Classification »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Sushma D S, Mohammed Alqhama, Aravind M, Jayanth A B, and Rakshith Kumar K. "Pest Detection and Classification in Peanut Crops." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1372–79. http://dx.doi.org/10.47392/irjaem.2024.0189.

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Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approa

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K.H, Sandeep. "Crop and Pest Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43290.

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Crop pests pose a hazard to agriculture by lowering yields and creating large losses. Timely intervention depends on prompt and precise pest identification. Convolutional Neural Networks (CNNs), a type of deep learning, are used in this study to effectively classify pests. To improve performance, the method places a strong emphasis on image preprocessing, accurate pest segmentation, and transfer learning. The algorithm is trained on a large dataset of photos of pests and non-pests to find distinctive characteristics for precise categorization. With an emphasis on improved image quality, segmen

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Myat, Mon Kyaw, San Nwe San, and Myint Yee Myint. "Pest Classification and Pesticide Recommendation System." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 2187–91. https://doi.org/10.5281/zenodo.3591203.

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Myanmar is an agricultural country and agriculture constitutes the largest sector of the economy. Recognizing of pests is a vital problem especially for farmers, agricultural researchers, and environmentalists. The proposed system is to classify the types of pest using the CNN model, which is often used when applying deep learning to image processing, and to recommend the most suitable pesticide according to the type of pest. This system will help to know easily information of pests and pesticides which should be used to the user. Using a public dataset of 1265 images of pests, a convolutional

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Doan, Thanh-Nghi. "Large-Scale Insect Pest Image Classification." Journal of Advances in Information Technology 14, no. 2 (2023): 328–41. http://dx.doi.org/10.12720/jait.14.2.328-341.

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Nyrop, Jan P., Michael R. Binns, and Wopke van der Werf. "Sampling for IPM Decision Making: Where Should We Invest Time and Resources?" Phytopathology® 89, no. 11 (1999): 1104–11. http://dx.doi.org/10.1094/phyto.1999.89.11.1104.

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Guides for making crop protection decisions based on assessments of pest abundance or incidence are cornerstones of many integrated pest management systems. Much research has been devoted to developing sample plans for use in these guides. The development of sampling plans has usually focused on collecting information on the sampling distribution of the pest, describing this sampling distribution with a mathematical model, formulating a sample plan, and sometimes, but not always, evaluating the performance of the proposed sample plan. For crop protection decision making, classification of dens

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P, Venkatasaichandrakanth, and Iyapparaja M. "GROUNDNUT CROP PEST DETECTION AND CLASSIFICATION USING COMPREHENSIVE DEEP-LEARNING MODELS." Suranaree Journal of Science and Technology 31, no. 1 (2024): 020028(1–17). http://dx.doi.org/10.55766/sujst-2024-01-e02544.

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Pests pose a significant threat to crops, leading to substantial economic losses and decreased food production. Early detection and accurate classification of pests in crops are crucial for effective pest management strategies. In this study, we propose a method for pest detection and classification in groundnut crops using deep learning models. In this research, we compare the performance of three deep learning models, namely Custom CNN [proposed], LeNet-5, and VGG-16, for groundnut pest detection and classification. A comprehensive dataset containing images of diverse groundnut crop pests, i

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Mr.R., Madanachitran. "DEEP LEARNING-BASED PEST CLASSIFICATION FOR PESTICIDE RECOMMENDATION IN AGRICULTURAL SYSTEMS." International Journal of Advances in Engineering & Scientific Research 10, no. 1 (2023): 17–27. https://doi.org/10.5281/zenodo.14924841.

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<em>Pest identification plays a crucial role in agricultural pest management, influencing pesticide selection and crop protection strategies. This study introduces Deep Pest Net, a deep learning-based model designed for efficient pest classification and pesticide recommendation. The proposed methodology consists of four key steps: data augmentation, image resizing, dataset partitioning, and model training/testing. To overcome data scarcity, augmentation techniques such as rotation, scaling, and translation were applied, enhancing model generalization. The DeepPestNet architecture comprises ele

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Binns, Michael R., Jan P. Nyrop, and Wopke Van Der Werf. "Monitoring Pest Abundance by Cascading Density Classification." American Entomologist 42, no. 2 (1996): 113–21. http://dx.doi.org/10.1093/ae/42.2.113.

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D. R, Mrs Dr Thirupurasundari. "Agriculture Pest Classification using Deep CNN Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 307–17. https://doi.org/10.22214/ijraset.2025.68207.

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Agricultural pests are spurs of economic, social and sorrowful environmental impacts around the globe. To control these pests proper identification and categorization is prudent in strategies used to tackle them. This work highlights the DeepPestNet, a CNN built specifically for accurately identifying nine classes of pests important in agriculture. Base onto the transfer learning of EfficientNetB0 which was developed to boost the performance of pest recognition, DeepPestNet has more convolutional and attention layers incorporated into the framework. Training and evaluation on this broad set sh

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S. Sabapathi, N. Vijayalakshmi. "A Unified Deep Learning Framework for Accurate Pest Detection and Classification in Agriculture." Journal of Information Systems Engineering and Management 10, no. 31s (2025): 599–612. https://doi.org/10.52783/jisem.v10i31s.5115.

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Introduction: Agriculture is an important role in sustaining human life and ensuring high quality food production is essential for economic growth. Among the main difficulties farmers encounter the rapid spread of insect and pest infestations which can significantly impact crop yields. Objectives: While, existing approaches have explored pest detection and classification, often suffer from inaccuracies and inefficiencies. To address these issues, this paper propose a unified Approach for PEST detection and classification model called SAMYNET (Segment Anything Model + YOLO8 + EfficientNet syste

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Amin, Javeria, Muhammad Almas Anjum, Rida Zahra, Muhammad Imran Sharif, Seifedine Kadry, and Lukas Sevcik. "Pest Localization Using YOLOv5 and Classification Based on Quantum Convolutional Network." Agriculture 13, no. 3 (2023): 662. http://dx.doi.org/10.3390/agriculture13030662.

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Pests are always the main source of field damage and severe crop output losses in agriculture. Currently, manually classifying and counting pests is time consuming, and enumeration of population accuracy might be affected by a variety of subjective measures. Additionally, due to pests’ various scales and behaviors, the current pest localization algorithms based on CNN are unsuitable for effective pest management in agriculture. To overcome the existing challenges, in this study, a method is developed for the localization and classification of pests. For localization purposes, the YOLOv5 is tra

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Liu, Liangliang, Jing Chang, Shixin Qiao, Jinpu Xie, Xin Xu, and Hongbo Qiao. "PMLPNet: Classifying Multi-Class Pests in Wild Environment via a Novel Convolutional Neural Network." Agronomy 14, no. 8 (2024): 1729. http://dx.doi.org/10.3390/agronomy14081729.

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Pest damage is a major factor in reducing crop yield and has negative impacts on the economy. However, the complex background, diversity of pests, and individual differences pose challenges for classification algorithms. In this study, we propose a patch-based neural network (PMLPNet) for multi-class pest classification. PMLPNet leverages spatial and channel contextual semantic features through meticulously designed token- and channel-mixing MLPs, respectively. This innovative structure enhances the model’s ability to accurately classify complex multi-class pests by providing high-quality loca

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Bastian, Ade, Adie Iman Nurzaman, Tri Ferga Prasetyo, and Sri Fatimah. "Roselle Pest Detection and Classification Using Threshold and Template Matching." Journal of Image and Graphics 11, no. 4 (2023): 330–42. http://dx.doi.org/10.18178/joig.11.4.330-342.

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Roselle is a fiber-producing plant that has broad benefits for health food, so many farmers are interested in starting to cultivate it. This study aims to design a rosella plant pest detection system to reduce the risk of crop failure or reduced yields of rosella calyx. The design of a system for detecting and classifying rosella pests uses the threshold method as a digital image processing method connected via the internet with information media applications and template matching to detect and classify pests on rosella plants. Detection of pests on rosella plants has been successfully built u

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Cheng, Zekai, and Wan Xia. "Fine-Grained Image Classification on Agricultural Pest Larvae." IOP Conference Series: Earth and Environmental Science 792, no. 1 (2021): 012037. http://dx.doi.org/10.1088/1755-1315/792/1/012037.

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Pattnaik, Gayatri, and Kodimala Parvathy. "Machine learning-based approaches for tomato pest classification." TELKOMNIKA (Telecommunication Computing Electronics and Control) 20, no. 2 (2022): 321. http://dx.doi.org/10.12928/telkomnika.v20i2.19740.

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Shin, Minsu, Yeongseo Ha, and Jaechang Shim. "Lightweight Lepidopteran Pest Classification Model Using Knowledge Distillation." Journal of Korea Multimedia Society 28, no. 2 (2025): 161–69. https://doi.org/10.9717/kmms.2025.28.2.161.

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Thuse, Sanjyot, and Meena Chavan. "Pest Classification using Morphological Processing in Deep Learning." International Journal of Electronics and Computer Applications 1, no. 1 (2024): 20–25. https://doi.org/10.70968/ijeaca.v1i1.thuse.

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Agriculture relies heavily on the prompt detection of pests. There are numerous technologies for identifying pests, but almost all of them are susceptible to misclassification due to inadequate lighting, background distractions, a diversity of collection techniques. Thus, pests that are only partially visible or oriented differently. This misclassification could result in a significant yield loss. We presented an architecture that would use skeletonization together with neural networks as classifiers to give excellent classification accuracy under the aforementioned parameters in order to alle

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Gayatri, Pattnaik, and Parvathi Kodimala. "Machine learning-based approaches for tomato pest classification." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 2 (2022): 321–28. https://doi.org/10.12928/telkomnika.v20i2.19740.

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Insect pests are posing a significant threat to agricultural production. They live in different places like fruits, vegetables, flowers, and grains. It impacts plant growth and causes damage to crop yields. We presented an automatic detection and classification of tomato pests using image processing with machine learning-based approaches. In our work, we considered texture features of pest images extracted by feature extraction algorithms like gray level co-occurrence matrix (GLCM), local binary pattern (LBP), histogram of oriented gradient (HOG), and speeded up robust features (SURF). The thr

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Sovia, Nabila Ayunda, and Ni Wayan Surya Wardhani. "ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 1237–48. http://dx.doi.org/10.30598/barekengvol18iss2pp1237-1248.

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Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensem

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A, Sathesh. "Spodoptera Litura Damage Severity Detection and Classification in Tomato Leaves." Journal of Innovative Image Processing 5, no. 1 (2023): 59–68. http://dx.doi.org/10.36548/jiip.2023.1.005.

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Agriculture plays a key role in global economy. Tomato is India's third most prioritized crop after potato and onion, but it is the world's second most prioritized crop after potato. Worldwide, India ranks second in tomato production. However, Tomato crop is constantly threatened by different pest infections. The most significant pest infection that highly affects the tomato crop yield is Spodoptera Litura. Emerging from the family of Noctuidae with vigorous eating pattern, this insect primarily feed on leaves and fruits by leaving the entire crop completely destroyed. Monitoring the pest spre

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Praharsha, Chittathuru Himala, Alwin Poulose, and Chetan Badgujar. "Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images." Sensors 24, no. 23 (2024): 7858. https://doi.org/10.3390/s24237858.

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Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (Solanum lycopersicum), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture. This paper explores using Convolutional Neural Networks (CNNs) to classify tomato pest images automatically. Specifically, we investigate

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Mandliya, Dilip, and Dr Manish Vyas. "Crop Infestation Classification Using MIL-Attention Based CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28309.

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Agriculture constantly faces various challenges including attacks from new pests and insects. With large farm sizes and plummeting manpower in the agricultural sector, it becomes challenging to continuously monitor crops for pest infestation. In this research paper, a specific type of pest attack known as the white fly attack has been investigated which affects a variety of crops. This paper presents a multiple instance learning based deep learning approach based on Convolutional Neural Networks for the detection of whitefly pests. A comparative analysis with conventional machine learning and

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Peng, Hongxing, Huiming Xu, Guanjia Shen, Huanai Liu, Xianlu Guan, and Minhui Li. "A Lightweight Crop Pest Classification Method Based on Improved MobileNet-V2 Model." Agronomy 14, no. 6 (2024): 1334. http://dx.doi.org/10.3390/agronomy14061334.

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This paper proposes PestNet, a lightweight method for classifying crop pests, which improves upon MobileNet-V2 to address the high model complexity and low classification accuracy commonly found in pest classification research. Firstly, the training phase employs the AdamW optimizer and mixup data augmentation techniques to enhance the model’s convergence and generalization capabilities. Secondly, the Adaptive Spatial Group-Wise Enhanced (ASGE) attention mechanism is introduced and integrated into the inverted residual blocks of the MobileNet-V2 model, boosting the model’s ability to extract b

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Nwonye, Charles A., K. Akpado, and D. O. Amaefule. "Development of a Specie-specific Bird Deterrent System using Birds Classifications by Convolutional Neural Network (CNN) Model." International Journal of Engineering Research & Science (IJOER) 10, no. 5 (2024): 07–18. https://doi.org/10.5281/zenodo.11452990.

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<strong><em>Abstract</em></strong><strong>—</strong> <em>A trained convolutional neural network (CNN) model was developed in this work for classification of birds that visit rice farms into harmful Sparrows or beneficial insectivorous birds, and the classification used in activating efficient pest bird deterrence. Different images of the prevalent pest sparrow were captured by high resolution camera, and used as datasets for the training of the CNN model for the pest bird identification. Since, 98% of sparrow birds are grain eaters and harmful to a rice farm, 2,000 images of different sp

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Ko, YuJin, HyunJun Lee, HeeJa Jeong, Li Yu, and NamHo Kim. "Deep Learning-based system for plant disease detection and classification." Korean Institute of Smart Media 12, no. 7 (2023): 9–17. http://dx.doi.org/10.30693/smj.2023.12.7.9.

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Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extrac

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Li, Chen, Tong Zhen, and Zhihui Li. "Image Classification of Pests with Residual Neural Network Based on Transfer Learning." Applied Sciences 12, no. 9 (2022): 4356. http://dx.doi.org/10.3390/app12094356.

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Agriculture is regarded as one of the key food sources for humans throughout history. In some countries, more than 90% of the population lives on agriculture. However, pests are regarded as one of the major causes of crop loss worldwide. Accurate and automated technology to classify pests can help pest detection with great significance for early preventive measures. This paper proposes the solution of a residual convolutional neural network for pest identification based on transfer learning. The IP102 agricultural pest image dataset was adopted as the experimental dataset to achieve data augme

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Li, Chen, Tong Zhen, and Zhihui Li. "Image Classification of Pests with Residual Neural Network Based on Transfer Learning." Applied Sciences 12, no. 9 (2022): 4356. http://dx.doi.org/10.3390/app12094356.

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Agriculture is regarded as one of the key food sources for humans throughout history. In some countries, more than 90% of the population lives on agriculture. However, pests are regarded as one of the major causes of crop loss worldwide. Accurate and automated technology to classify pests can help pest detection with great significance for early preventive measures. This paper proposes the solution of a residual convolutional neural network for pest identification based on transfer learning. The IP102 agricultural pest image dataset was adopted as the experimental dataset to achieve data augme

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Ebrahimi, M. A., M. H. Khoshtaghaza, S. Minaei, and B. Jamshidi. "Vision-based pest detection based on SVM classification method." Computers and Electronics in Agriculture 137 (May 2017): 52–58. http://dx.doi.org/10.1016/j.compag.2017.03.016.

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Kusrini, Kusrini, Suputa Suputa, Arief Setyanto, et al. "Data augmentation for automated pest classification in Mango farms." Computers and Electronics in Agriculture 179 (December 2020): 105842. http://dx.doi.org/10.1016/j.compag.2020.105842.

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S. Sandhya Devi, R., V. R. Vijay Kumar, and P. Sivakumar. "EfficientNetV2 Model for Plant Disease Classification and Pest Recognition." Computer Systems Science and Engineering 45, no. 2 (2023): 2249–63. http://dx.doi.org/10.32604/csse.2023.032231.

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Kim, Ga-Eun, and Chang-Hwan Son. "Multiscale Crosss Attention Vision Transformer for Pest Image Classification." Journal of Korean Institute of Information Technology 21, no. 7 (2023): 77–84. http://dx.doi.org/10.14801/jkiit.2023.21.7.77.

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Priya, S. Lakshmi, and R. Subhashini. "Deep-Learning Model for Wheat Disease and Pest Classification." International Journal of Microsystems and IoT 2, no. 5 (2024): 871–80. https://doi.org/10.5281/zenodo.13132457.

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Severe pests and diseases in wheat are caused by global change and natural disturbances. This results in a significant loss of yield and quality. Therefore, ecological problems will be solved by early detection of diseases and pests. In prevailing techniques, different types of wheat diseases and pests were not studied completely. Hence, a Deep-Learning (DL) framework for wheat field disease and pest classification in leaves via satellite images using Non-monotonic Correlated-Extreme Learning Machine (NC-ELM) is proposed. Primarily, the farm field’s satellite images are given as input to

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Li, Zhiyong, Xueqin Jiang, Xinyu Jia, Xuliang Duan, Yuchao Wang, and Jiong Mu. "Classification Method of Significant Rice Pests Based on Deep Learning." Agronomy 12, no. 9 (2022): 2096. http://dx.doi.org/10.3390/agronomy12092096.

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Rice pests are one of the main factors affecting rice yield. The accurate identification of pests facilitates timely preventive measures to avoid economic losses. Some existing open source datasets related to rice pest identification mostly include only a small number of samples, or suffer from inter-class and intra-class variance and data imbalance challenges, which limit the application of deep learning techniques in the field of rice pest identification. In this paper, based on the IP102 dataset, we first reorganized a large-scale dataset for rice pest identification by Web crawler techniqu

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Pavan, J. S., B. L. Raghunandan, Nainesh B. Patel, C. N. Rajarushi, M. R. Raiza Nazrin, and K. S. Ishwarya Lakshmi. "Baculoviruses in Integrated Pest Management of Fall Armyworm, (Spodoptera frugiperda) (Lepidoptera: Noctuidae): Structure, Classification and Application." Journal of Advances in Biology & Biotechnology 27, no. 9 (2024): 261–71. http://dx.doi.org/10.9734/jabb/2024/v27i91296.

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Baculoviruses are crucial biological agents in integrated pest management (IPM), particularly for controlling lepidopteran pests in agriculture. These are highly specific, eco-friendly viruses characterized by rod-shaped nucleocapsids containing a protein-encased genome. The Baculoviridae family comprises four genera: Alphabaculovirus, Betabaculovirus, Gammabaculovirus, and Deltabaculovirus, each targeting specific insect orders. Nucleo polyhedron virus (NPVs) and granuloviruses (GVs) are extensively used in pest management due to their high virulence and specificity, ensuring safety for non-t

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Srilekha, N., V. Tejaswini, M. Sneha, Abdul Aas Shaik, Sohail Zahid, and Zaheer Shaik. "Deep Learning for Pest Detection and Extraction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42777.

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Pest infestations pose a significant challenge to agriculture, resulting in substantial crop damage and economic losses. Traditional pest detection systems primarily rely on Convolutional Neural Networks (CNNs) for image classification. While CNNs are effective at categorizing images and identifying pests, they face limitations in handling scenarios involving multiple pests, varying orientations, and complex backgrounds. Additionally, CNNs lack the ability to localize pests within images, providing only image-level classifications rather than detailed spatial information. To address these limi

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Dimas Saputra, Archamul Fajar Pratama, Muhammad Dawam Fakhri, Muhammad Ahsanur Rafi, and Fetty Tri Anggraeny. "CLASSIFICATION OF INSECT PESTS IN AGRICULTURE USING INCEPTION-RESNET-V2 ARCHITECTURE." Antivirus : Jurnal Ilmiah Teknik Informatika 19, no. 1 (2025): 41–51. https://doi.org/10.35457/antivirus.v19i1.4107.

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Object recognition in images is a major challenge in digital image processing with wide applications, including agriculture. This research aims to develop a Convolutional Neural Network (CNN) model based on the Inception-ResNet-V2 architecture for insect pest classification in agriculture. The dataset contains 1,591 images from 13 pest classes, which were processed through preprocessing stages such as resizing, normalization, and augmentation to enhance data quality and variation. The model training process was conducted for 10 epochs, resulting in an accuracy of 89.52% with a loss of 0.4024.

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De Oliveira Costa, Pedro Lucas, Thiago Matheus De Oliveira Costa, Larissa Ferreira Rodrigues Moreira, Leandro Henrique Furtado Pinto Silva, and João Fernando Mari. "Classification of Agricultural Pests Through Digital Images Using Deep Learning." Revista de Informática Teórica e Aplicada 32, no. 1 (2025): 18–25. https://doi.org/10.22456/2175-2745.143520.

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In the agricultural sector, pest detection is vital, and given the susceptibility of human analysis to errors, deep learning solutions such as Convolutional Neural Networks (CNNs) provide a promising alternative. Classifying insect pests is challenging due to the high variability among species across different regions and their various life stages. In this study, we evaluate several deep learning models and training strategies for automatic pest image classification. We analyze four CNN architectures—AlexNet, ResNet-50, EfficientNet, and Vision Transformer (ViT). Following a hyperparameter opt

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Huang, Qixuan. "Comparison of Deep Transfer Learning Models for Pest Image Classification in Agriculture." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 9–16. http://dx.doi.org/10.54097/kxbxjn03.

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In the field of agriculture, crops are susceptible to attacks by pests. Accurately identifying and classifying crop pests, especially in their early stages of growth, is a significant challenge. Convolutional neural networks have become effective instruments for agricultural pest classification because of their capacity to extract and learn intricate information from photos. This study explores the use of transfer learning methods to compare efficient models with complex models to improve the effectiveness of pest and disease classification. The purpose of this study is to compare efficient mo

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Li, Qiming. "Optimizing Token Fusion Mechanisms in Swin Transformer for Improved Feature Representation and Fine-Grained Insect Classification." Applied and Computational Engineering 142, no. 1 (2025): 151–60. https://doi.org/10.54254/2755-2721/2025.kl22294.

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Effective pest monitoring is critical for ensuring global food security, as insect pests pose significant threats to crop yields and agricultural sustainability. Traditional pest identification methods rely on manual inspection, which is time-consuming, labor-intensive, and susceptible to human error. Deep learning, particularly Convolutional Neural Networks (CNNs), has been widely applied to automate insect classification; however, these models exhibit limitations in capturing long-range dependencies and hierarchical feature representations. Transformer-based architectures, such as Swin Trans

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Elci, Brundha, and Moulyashree S. "Pest Detection System for Farmers." International Research Journal of Computer Science 12, no. 04 (2025): 171–76. https://doi.org/10.26562/irjcs.2025.v1204.10.

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This paper presents lightweight web-based pest detection software that aids in the early detection of crop pests using image classification techniques. The system is designed to support real-time predictions, user-friendly dashboards, and access control based on role-based logins. Built using React.js, Node.js, and MySQL, it can support up to 1000 user records with high efficiency. The software also integrates graphical visualizations using Recharts, helping users track pest prediction history and class distribution with confidence levels. This solution aims to improve agricultural productivit

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Ibrahim, Mohd Firdaus, Siti Khairunniza-Bejo, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, and Mohammad Aufa Mhd Bookeri. "Deep CNN-Based Planthopper Classification Using a High-Density Image Dataset." Agriculture 13, no. 6 (2023): 1155. http://dx.doi.org/10.3390/agriculture13061155.

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Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centim

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Dinca, Marius Alexandru, Dan Popescu, Loretta Ichim, and Nicoleta Angelescu. "Ensemble of Efficient Vision Transformers for Insect Classification." Applied Sciences 15, no. 13 (2025): 7610. https://doi.org/10.3390/app15137610.

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Real-time identification of insect pests is an important research direction in modern agricultural management, directly influencing crop health and yield. Recent advances in computer vision and deep learning, especially vision transformer (ViT) architectures, have demonstrated great potential in addressing this challenge. The present study explores the possibility of combining some ViT models for the insect pest classification task to improve system performance and robustness. Two popular and widely known datasets, D0 and IP102, which consist of diverse digital images with complex contexts of

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Tang, Wentao, and Zelin Hu. "Potato Disease and Pest Question Classification Based on Prompt Engineering and Gated Convolution." Agriculture 15, no. 5 (2025): 493. https://doi.org/10.3390/agriculture15050493.

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Currently, there is no publicly available dataset for the classification of potato pest and disease-related queries. Moreover, traditional query classification models generally adopt a single maximum-pooling strategy when performing down-sampling operations. This mechanism only extracts the extreme value responses within the local receptive field, which leads to the degradation of fine-grained feature representation and significantly amplifies text noise. To address these issues, a dataset construction method based on prompt engineering is proposed, along with a question classification method

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Yuan, Yi, and Xiangyun Hu. "RANDOM FOREST AND OBJECTED-BASED CLASSIFICATION FOR FOREST PEST EXTRACTION FROM UAV AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1093–98. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1093-2016.

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Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control and exterminate it has not effective enough so far now. The infected areas by it have continuously spreaded out at present. Thus the introduction of spatial information technology is highly demanded. It is very effective to examine the spatial distribution characteristics that can establish timely proper strategies for control against pests by periodically figuring out the infected situations as

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Yuan, Yi, and Xiangyun Hu. "RANDOM FOREST AND OBJECTED-BASED CLASSIFICATION FOR FOREST PEST EXTRACTION FROM UAV AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 1093–98. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1093-2016.

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Forest pest is one of the most important factors affecting the health of forest. However, since it is difficult to figure out the pest areas and to predict the spreading ways just to partially control and exterminate it has not effective enough so far now. The infected areas by it have continuously spreaded out at present. Thus the introduction of spatial information technology is highly demanded. It is very effective to examine the spatial distribution characteristics that can establish timely proper strategies for control against pests by periodically figuring out the infected situations as

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Yu, Junwei, Yi Shen, Nan Liu, and Quan Pan. "Frequency-Enhanced Channel-Spatial Attention Module for Grain Pests Classification." Agriculture 12, no. 12 (2022): 2046. http://dx.doi.org/10.3390/agriculture12122046.

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For grain storage and protection, grain pest species recognition and population density estimation are of great significance. With the rapid development of deep learning technology, many studies have shown that convolutional neural networks (CNN)-based methods perform extremely well in image classification. However, such studies on grain pest classification are still limited in the following two aspects. Firstly, there is no high-quality dataset of primary insect pests specified by standard ISO 6322-3 and the Chinese Technical Criterion for Grain and Oil-seeds Storage (GB/T 29890). The images

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A., Pushpa Athisaya Sakila Rani, and N. Suresh Singh Dr. "Pest and Disease Identification in Paddy by Symptomatic Assessment of The Leaf using Hybrid CNN-LSTM Algorithm." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 6 (2022): 7–10. https://doi.org/10.35940/ijrte.F6795.0310622.

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<strong>Abstract: </strong>The crop damage is caused by various types of pests that feed on the leaf, stem, roots or entire part of the plants and also by fungal, bacterial and viral infections. In most cases, the diseases are transmitted from one plant to another by vectors. The pests act as vectors in spreading most of the viral infections. It is necessary to identify the disease incidence or pest infestation in the early stages itself and contains its spread before it causes any damage to plants. Several machine and deep learning approaches are involved in rice disease and pest identificati

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Arame, Mohamed, Issam Meftah Kadmiri, Francois Bourzeix, Yahya Zennayi, Rachid Boulamtat, and Abdelghani Chehbouni. "Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco." Agronomy 15, no. 5 (2025): 1106. https://doi.org/10.3390/agronomy15051106.

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This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists of the application of these techniques to chickpea plants in controlled laboratory conditions using a natural infestation protocol, something not previously explored. The two major methodologies adopted in the approach are as follows: (1) spectral feature-based classification using hyperspectra

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Nguyen, Tuan, Quoc-Tuan Vien, and Harin Sellahewa. "An Efficient Pest Classification In Smart Agriculture Using Transfer Learning." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 8, no. 26 (2021): 168227. http://dx.doi.org/10.4108/eai.26-1-2021.168227.

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Durgabai, R. P. L., P. Bhargavi, and S. Jyothi. "Classification of Pests for Rice Crop Using Big Data Analytics." Asian Journal of Computer Science and Technology 8, no. 3 (2019): 27–31. http://dx.doi.org/10.51983/ajcst-2019.8.3.2737.

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Data, in today’s world, is essential. The Big Data technology is rising to examine the data to make fast insight and strategic decisions. Big data refers to the facility to assemble and examine the vast amounts of data that is being generated by different departments working directly or indirectly involved in agriculture. Due to lack of resources the pest analysis of rice crop is in poor condition which effects the production. In Andhra Pradesh rice is cultivated in almost all the districts. The goal is to provide better solutions for finding pest attack conditions in all districts using Big D

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