Open Access
Issue |
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
|
|
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Article Number | 01032 | |
Number of page(s) | 7 | |
Section | Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development | |
DOI | https://doi.org/10.1051/shsconf/202521601032 | |
Published online | 23 May 2025 |
- P. Venkatasaichandrakanth, M. Iyapparaja, A survey on pest detection and classification in field crops using artificial intelligence techniques. International Journal of Intelligent Robotics and Applications, 1–26 (2024). https://doi.org/10.1007/s41315-024-00347-w [Google Scholar]
- P.K. Paul, R.R. Sinha, P.S. Aithal, B. Aremu, R. Saavedra, Agricultural Informatics: An Overview of Integration of Agricultural Sciences and Information Science. Indian Journal of Information Sources and Services 10(1), 48–55 (2020). https://doi.org/10.2139/ssrn.3764184 [CrossRef] [Google Scholar]
- Hussain, P.B. Srikaanth, Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model. KSII Transactions on Internet & Information Systems 18 (2024) [Google Scholar]
- M. Rao, S. Kumar, K. Rao, Effective medical leaf identification using hybridization of GMMCNN. International Journal of Experimental Research and Review 32, 115–123 (2023) [CrossRef] [Google Scholar]
- M.A. Ali, A.K. Sharma, R.K. Dhanaraj, Heterogeneous features and deep learning networks fusion-based pest detection, prevention and controlling system using IoT and pest sound analytics in a vast agriculture system. Computers and Electrical Engineering 116, 109146 (2024). https://doi.org/10.1016/j.compeleceng.2024.109146 [CrossRef] [Google Scholar]
- A. Radhika, M.S. Masood, Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique. Journal of Internet Services and Information Security 12(4), 177–196 (2022) [CrossRef] [Google Scholar]
- J. Amin, M.A. Anjum, R. Zahra, M.I. Sharif, S. Kadry, L. Sevcik, Pest localization using yolov5 and classification based on quantum convolutional network. Agriculture 13, 662 (2023). https://doi.org/10.3390/agriculture13030662 [CrossRef] [Google Scholar]
- P. Angin, M.H. Anisi, F. Göksel, C. Gürsoy, A. Büyükgülcü, Agrilora: a digital twin framework for smart agriculture. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 11(4), 77–96 (2020) [Google Scholar]
- Z. Anwar, S. Masood, Exploring deep ensemble model for insect and pest detection from images. Procedia Computer Science 218, 2328–2337 (2023). https://doi.org/10.1016/j.procs.2023.01.190 [CrossRef] [Google Scholar]
- K. Veerasamy, E.J. Thomson Fredrik, Intelligence System towards Identify Weeds in Crops and Vegetables Plantation Using Image Processing and Deep Learning Techniques. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14(4), 45–59 (2023) [CrossRef] [Google Scholar]
- R. Hadipour-Rokni, E.A. Asli-Ardeh, A. Jahanbakhshi, S. Sabzi, Intelligent detection of citrus fruit pests using machine vision system and convolutional neural network through transfer learning technique. Computers in Biology and Medicine 155, 106611 (2023). https://doi.org/10.1016/j.compbiomed.2023.106611 [CrossRef] [Google Scholar]
- Y. Camgözlü, Y. Kutlu, Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Natural and Engineering Sciences 8(3), 214–232 (2023) [CrossRef] [Google Scholar]
- H. Peng, H. Xu, Z. Gao, Z. Zhou, X. Tian, Q. Deng, et al., Crop pest image classification based on improved densely connected convolutional network. Frontiers in Plant Science 14, 1133060 (2023). https://doi.org/10.3389/fpls.2023.1133060 [CrossRef] [Google Scholar]
- K. Veerasamy, E.T. Fredrik, Intelligent Farming based on Uncertainty Expert System with Butterfly Optimization Algorithm for Crop Recommendation. Journal of Internet Services and Information Security 13(4), 158–169 (2023) [CrossRef] [Google Scholar]
- S. Pournima, C. Priyatharsini, G. Kirubasri, J. Manikandan, Hybrid BILSTM Network for Improving Crop Pest Classification, in Proceedings of the 2023 International Conference on Computer Communication and Informatics (ICCCI), 1–5 (2023) [Google Scholar]
- M. Chithambarathanu, M. Jeyakumar, Survey on crop pest detection using deep learning and machine learning approaches. Multimedia Tools and Applications 82, 42277–42310 (2023). https://doi.org/10.1007/s11042-023-15221-3 [CrossRef] [Google Scholar]
- M. Francisco, F. Ribeiro, J. Metrolho, R. Dionisio, Algorithms and models for automatic detection and classification of diseases and pests in agricultural crops: A systematic review. Applied Sciences 13, 4720 (2023). https://doi.org/10.3390/app13084720 [CrossRef] [Google Scholar]
- M. Dai, M.M.H. Dorjoy, H. Miao, S. Zhang, A new pest detection method based on improved YOLOv5m. Insects 14, 54 (2023). https://doi.org/10.3390/insects14010054 [CrossRef] [Google Scholar]
- V. Radhika, R. Ramya, R. Abhishek, Machine learning approach-based plant disease detection and pest detection system, in International Conference on Communications and Cyber Physical Engineering 2018, 191–200 (2023) [Google Scholar]
- S. Khalid, H.M. Oqaibi, M. Aqib, Y. Hafeez, Small pests detection in field crops using deep learning object detection. Sustainability 15, 6815 (2023). https://doi.org/10.3390/su15086815 [CrossRef] [Google Scholar]
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