| Abstrakt: |
Millets, often referred to as "Nutri Cereals," hold significant potential for enhancing future food availability. Over the past 50 years, advancements such as the use of fertilizers, insecticides, and controlled irrigation have markedly increased the productivity of pearl millet crops worldwide. According to the 2020 review report by the Directorate of Millets Development, 693 million hectares of agricultural land are dedicated to pearl millet cultivation. However, managing crop diseases remains a substantial challenge for farmers. Effective crop disease management is crucial for sustainable agriculture, yet traditional algorithms often struggle with accurate detection and computational inefficiency especially in the case of golden cereal termed as "Pearl Millet". Our study introduces a novel approach for disease detection in pearl millet crops using aerial imagery, named Split-Attention Networks with Disentangled Nonlocal and Edge Supervision (SANDNES). This method leverages advanced neural network architectures to enhance disease detection accuracy and efficiency. Our comprehensive numerical evaluations demonstrate that SANDNES outperforms traditional U-Net and U-Net + Nonlocal baseline models Comprehensive evaluations demonstrate SANDNES' exceptional performance, achieving an impressive average F1 score of 89.04%, significantly surpassing other methods in accuracy and efficiency. Specifically, SANDNES achieved notable performance improvements, with F1 scores of 94.88% for healthy crops, 85.96% for honeydew, 82.97% for smut, and 92.69% for ergot. This method leverages split-attention mechanisms for precise feature localization, with Disentangled Nonlocal and Edge Supervision enhancing contextual reasoning and boundary delineation. SANDNES superior computational efficiency and high accuracy make it suitable for real-time precision agriculture applications. [ABSTRACT FROM AUTHOR] |
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