1. New segmentation approach for effective weed management in agriculture
- Author
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Mohammed Habib, Salma Sekhra, Adil Tannouche, and Youssef Ounejjar
- Subjects
Images segmentation ,Computer vision ,Convolutional neural networks ,Agricultural images ,Weed detection ,Smart farming ,Agriculture (General) ,S1-972 ,Agricultural industries ,HD9000-9495 - Abstract
Accurate weed detection in agricultural images is a crucial challenge for improving crop management practices and reducing chemical usage. In this study, we propose an innovative segmentation model called DWUNet, inspired by popular architectures and incorporating the latest advances in the state of the art. Our model delivers remarkable accuracy, with a Jaccard index reaching 0.825, while ensuring fast inference speed of only 8 ms per image, thus providing an optimal solution for real-time applications. By comparing DWUNet to several state-of-the-art models, we demonstrate its superiority in terms of accuracy and efficiency. Furthermore, a qualitative analysis of the visual results confirms DWUNet's ability to accurately detect weeds and generalize results beyond the training data. This study represents a significant advancement in the field of precision agriculture, providing a powerful tool for sustainable crop management and reducing environmental impact.
- Published
- 2024
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