2026· ITM Web of Conferences· Vol 89, pp. 01001· 0 citations· 6 references
TL;DR
The rice disease identification method proposed in this paper provides a reference for the deployment and application of the method in different light conditions in the field and achieves test average accuracy of 83.9% and a maximum validation accuracy of 88.0% on the testing sets.
Abstract
Rice is one of the most significant crops in all the world agriculture, diseases and pests of rice seriously threaten grain production. Identifying and classifying them accurately in the field is essential for prevention. However, the existing models often encounter issues such as unequal between lightweight deployment and recognition performance, weak robustness to various lighting conditions and insufficient generalization ability due to class imbalance. To address this issue, the paper proposed a lightweight method for rice diseases' classification based on the optimized MobileNetV2. Its core structure adopted MobileNetV2 as the backbone to retain the advantage of depthwise separable convolution lightweight model and added an SE attention layer after feature extraction stage to enhance the model's focus on lesion regions. Using Test-time- augmentation (TTA) further improves the model's robustness to lighting and shooting angle interference. Based on the experiment results, the proposed model achieves a test average accuracy of 83.9% and a maximum validation accuracy of 88.0% on the testing sets. Under extreme lighting conditions, the performance loss percentage is controlled at 20.78%. The rice disease identification method proposed in this paper provides a reference for the deployment and application of the method in different light conditions in the field.
The proposed MultiCotNet framework provides a scalable and reliable solution for early cotton disease detection and can support intelligent agricultural monitoring systems for timely disease management and improved crop productivity.
Sabari Nathan, S. A., K. S et al.· Journal of Cotton Research· 0 citations
A scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics and enables deployment on smartphones, drones, and edge devices for smart agriculture applications.
B. Venkata Ramulu, Geeta Tripathi· International Journal of Eng...· 0 citations
Wheat planting areas face complex field environments, while embedded edge devices are restricted by limited computing power. Conventional detection models fail to balance recognition accuracy and real-time inference speed for multiple wheat diseases. This paper proposes a lightweight multi-disease detection algorithm based on improved YOLOv11. Taking YOLOv11s as the baseline, lightweight optimizations are conducted on the backbone, neck and detection head. A self-built wheat disease image dataset is constructed for model evaluation. The improved model achieves drastically reduced parameters and computational overhead, outperforming original lightweight YOLOv11 variants in detection accuracy. Ablation experiments verify the independent performance gain of each optimized module. After ONNX export and TensorRT FP16 quantization, the model is deployed on Raspberry Pi 5 and attains an ultra-high inference frame rate. The proposed lightweight algorithm addresses the challenge of real-time wheat disease detection on field edge terminals, delivering a practical high-precision, low-latency solution for intelligent crop disease diagnosis in farmlands.
Guan-Long Zhu· Eurasia journal of science a...· 0 citations
Results indicate that the proposed model provides an effective method for tomato leaf disease recognition in intelligent agricultural monitoring scenarios and demonstrates that the proposed attention mechanism can enhance lesion localization and suppress irrelevant background responses.
Jinqiao Nong, Yushan Lin· Advances in Engineering Tech...· 0 citations
The FRCNN-FCMWeight model provides strong support for agricultural use cases and improves overall model generalization and enhances training robustness and identifies diseased regions effectively under highly variable field conditions.
Shashank Yadav, A. S. Tewari· Journal of Crop Health· 0 citations
The stable production of crops such as corn, wheat, soybeans, and canola is increasingly threatened by widespread pest infestations. Conventional manual pest surveys are hampered by low operational efficiency, subjective assessment bias, and delayed feedback, thereby impeding their ability to satisfy the demands of precision agriculture. To address these challenges, we proposes an intelligent pest detection framework based on EfficientNet and Feature Pyramid Network (FPN) for fast and accurate field pest identification. EfficientNet is adopted as the lightweight attention-embedded backbone to extract hierarchical features, and multi-scale detection plus hierarchical FPN fusion are integrated to improve recognition performance for tiny, inconspicuous pests. The experimental results on 37 common pest species in field crops showed that the proposed model achieves a mean average precision at Intersection-over-Union (IoU) threshold 0.5 (mAP@0.5) of 98.89%, 1.57% average recognition error rate, and with an average inference time of merely 0.048 s per image, balancing outstanding detection accuracy and real-time performance. Furthermore, this approach delivers a lightweight, reliable, and automated monitoring solution for field pest surveillance, thereby facilitating data-driven, precise pest management and advancing the practice of sustainable, green precision agriculture.
He Zhang, Xiao-Chen Liu, Chenguang Wang et al.· Agronomy· 0 citations
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