Development of an automatic monitoring system for rice light-trap pests based on machine vision

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Multiple Object Tracking in Native Bee Hives: A Case Study with Jataí in the Field

Leocádio, R. R. V., Segundo, A. K. R., & Pessin, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45392-2_12 · Cited by 2 · see citing works

The effect of using a fried rice machine on product effectiveness and productivity

Sutiadiningsih, A., Dewi, I. H. P., Fahmi, H. Z., Kurniawan, W. D., & Ma’arof, M. I. N. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202345002003 · Cited by 1 · see citing works

Few-shot driven construction method of a large-scale light-trapped insect annotation data based on vision foundation models and self-supervised learning

You, Y., Feng, Z., Wang, Z., Li, L., Luo, J., Lü, J., Zhang, H., Yang, B., Liu, S., & Yao, Q. (2025) · Journal of Integrative Agriculture · doi:10.1016/j.jia.2025.08.020 · Cited by 1 · see citing works

An integrated edge AI prototype for smart agriculture: real-time pest detection, physical trapping, and multi-node deployment analysis under field uncertainty

Wang, K., Guo, S., Yang, X., Lin, B., Cai, W., Tang, R., & Lin, J. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2026.1853368 · Cited by 1 · see citing works

Object pest detection method based on lightweight SSD_RA algorithm

Li, S., Peng, H., & Yuan, J. (2022) · International Conference on Networks, Communication and Computing (ICNCC) · doi:10.1145/3579895.3579911 · Cited by 1 · see citing works

Comparative Analysis of VGG and ResNet for the Prediction of Rice Leaf Disease

Parasa, G., Arulselvi, M., & Razia, S. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220897 · Cited by 1 · see citing works

SESC-YOLO: Enhanced YOLOV5 for Detecting Defects on Steel Surface

Kavitha, S., Baskaran, K. R., & Santhiya, K. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-4577-1_17 · Cited by 1 · see citing works

Steel Defect Detection Based on Attention Mechanism

Li, W., Yao, X., Yang, W., & Li, Y. (2023) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi59194.2023.10269960 · Cited by 1 · see citing works

Mechatronic System for ESP32CAM OpenCV Rice Plant Pest Detection

Tukino, T., Nanda, R. A., Fauzi, A., Suhara, A., & Khaerudin, M. (2023) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic60109.2023.10381956 · Cited by 1 · see citing works

Cotton Pest Trap Using Machine Learning Models

Vasudeva Rao, P., V, Pakkala, P. G. R., Akhila Thejaswi, R., SHETTY, S. A., Lakshmisha, L., Naik, K., Shetty, S. S., Shetty, S. S., & Balamurali, M. (2024) · International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC) · doi:10.1109/cosmic63293.2024.10871670 · Cited by 1 · see citing works

SVM- and K-NN-Based Paddy Plant Insects Recognition

Nagarathinam, T., & Elangovan, V. R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-3153-5_3

Basketball Posture Recognition Method in Physical Education Based on Machine Vision

Li, X., & Wang, Z. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-18123-8_15

Baxter Robot Shape Feature Recognition Algorithm Based on Machine Vision

Mo, Y. (2023) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece57866.2023.10151284

Machine Vision for Smart Trap Bandwidth Optimization and New Threat Identification

Moura, P., Pinheiro, I., Terra, F., Pinho, T. M., & dos Santos, F. N. (2023) · International Electronic Conference on Agronomy (IECAG) · doi:10.3390/iecag2023-15993

Machine Vision Applied to Entomology

Palma, G. R., Hackett, C., & Markham, C. (2023) · Entomology in focus · doi:10.1007/978-3-031-43098-5_9

Exploring the Utility of Light Traps for Early Detection and Management of Rice Insect Pests

Atta, B., Sabir, A. M., Gogi, M. D., Farooq, M. A., Ijaz, M., Awan, T. H., Ayub, M. A., Saleem, M. U., & Nasiba, A. (2024) · Punjab University journal of zoology · doi:10.17582/journal.pujz/2024/39.1.21.32

Machine Learning and Deep Learning Techniques for Pest and Disease Detection in Sustainable Agriculture: A Study

Guhan, K., Joy, H. K., Sridevi, R., Jayarani, A. E. A., & Vanusha, D. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-5791-6_30

A Framework for Insect Detection and Classification in Paddy Images

Vinodhkumar, B., Ravi, A., & Sivakumar, S. (2024) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart63812.2024.10882567

IoT and Neural Network based Intelligent Paddy Field Monitoring and Management System

Archana, V., Dharmambal, M., Suguna, A., Seema, P., Abinaya, A., & Gayathri, G. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252571

Field-Based Image Acquisition and Deep Learning Framework for Early-Stage Detection of Rice Leaf Folder (Cnaphalocrocis Medinalis) Integrating Environmental Metadata

Janarththanan, J., Mikunthan, G., Liyanage, S. R., & Sharmilan, T. (2025) · International Conference on Innovations in Engineering, Science and Technology for Sustainable Development (ICEST) · doi:10.1109/icest65883.2025.11428686

Deep Learning in Plant Pest Control

Swapna, B., Kameswaran, S., & Ramakrishna, M. (2026) · CABI eBooks · doi:10.1079/9781836991328.0017

A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images

Li, Z., Lin, Z., Liu, X., & Li, Y. (2026) · Sensors · doi:10.3390/s26144576

A qPCR-Machine Learning Framework for Quantifying Species Composition in Mixed Light-Trap Samples of Nilaparvata Planthoppers

Yang, G., Cilin, W., Ju, L., Tang, J., & Shuhua, L. (2026) · Rice Science · doi:10.1016/j.rsci.2026.09.005

Plant‐derived eDNA metabarcoding is an effective approach for monitoring invertebrate rice pests

Chen, J., Xu, C. C. Y., Wang, L., Song, X., Shi, A., & Yang, T. (2026) · Agricultural and Forest Entomology · doi:10.1111/afe.70082

YOLO26-USW: A Slice-Aided Semantic–Detail Fusion Detector for Small Insects in High-Resolution Agricultural Light-Trap Images

Wei, B., Li, H., Yang, Y., Li, X., Li, F., Zhang, T., & Guo, L. (2026) · Agronomy · doi:10.3390/agronomy16202043

An Insect Imaging System to Automate Rice Light-Trap Pest Identification

Qing, Y. A. O., Lv, J., Qing-jie, L. I. U., Diao, G., Yang, B., Chen, H., & Tang, J. (2012) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(12)60089-6 · Cited by 99 · see citing works

Automatic Identification of Rice Light-trapped Pests Based on Images

Ding-xian, X. (2015) · Zhongguo shuidao kexue · Cited by 4 · see citing works

Intelligent Monitoring System of Migratory Pests Based on Searchlight Trap and Machine Vision

Sun, G., Liu, S., Luo, H., Feng, Z., Yang, B., Luo, J., Tang, J., Yao, Q., & Xu, J. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.897739 · Cited by 30 · see citing works

Pest Identification using Image Processing Techniques in Detecting Image Pattern through Neural Network

Miranda, J. L., Gerardo, B. D., & Tanguilig, B. T. (2014) · Cited by 11 · see citing works

A Field-Deployable Visual Monitoring Device for Measuring Nocturnal Phototactic Rhythm of Rice Pests

Fu, Y., Shu, L., Li, K., Dai, F., Han, R., Lin, W., Fang, J., & Meng, C. (2026) · Electronics · doi:10.3390/electronics15112425