Innovative Method for Camouflaged Wildlife Segmentation in Agricultural Practices

Accurately identifying concealed wildlife, including arachnids, serpents, and arthropods, is paramount for proficient agricultural management. This study introduces a deep learning approach utilizing transformers for segmenting images of insects and animals, specifically a ddressing c hallenges such as size discrepancies, color and texture resemblances to the environment, ambiguous outlines, and transparent body parts. The proposed model, featuring a transformer-based architecture with an efficient attention mechanism, achieved promising results with a Dice score of 0.952, MAE of 0.041, and $\Gamma$-measure of 0.570. Our model surpasses human perception in detecting and segmenting elusive organisms, enhancing intelligent recognition for comprehensive insect detection. Farmers can detect concealed fauna threatening agricultural produce with precise identification, leading to increased crop yield and advanced pest management approaches.