DISTRIBUTED DEEP LEARNING MODELS FOR PRECISE WEED DETECTION IN CROP FIELDS
DOI:
https://doi.org/10.30890/2709-2313.2025-43-01-003Keywords:
distributed deep learning, weed detection, federated learning, precision agriculture, computer vision, IoU, F1-scoreAbstract
This paper presents distributed deep learning models for accurate weed detection in agricultural crops such as wheat, soybean, corn, and potato.The study explores federated learning architectures combining ResNet50, U-Net++, Swin Transformer, and YOLOv8References
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Li, X. et al. Federated Learning for Smart Agriculture. IEEE IoT Journal, 2023.
Wang, Y. et al. Distributed Deep Learning for Precision Weed Control. Sensors, 2023.
Zhao, H. et al. Swin Transformer for Crop-Weed Segmentation. Frontiers in Plant Science, 2025.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
