ALR-AM-Net: Efficient fruit disease identification using Aniostropic diffusion with Multi modal feature fusion
Keywords:
Anisotropic Diffusion, ALR-AM-Net Model, Attention Mechanism, Fruit Disease IdentificationAbstract
Identifying fruit diseases is essential to prevent crop losses and reduce economic challenges for farmers and traders. Effective detection ensures quality standards, supports trade, and protects crops and ecosystems from disease spread. This study focuses on developing a fruit disease identification using datasets of Citrus, Guava,Papaya, and Apple fruits. These fruits are affected by various diseases. Citrus fruits are susceptible to blackspot,canker, screening, and scab. Guava fruits can be afflicted by Phytophthora, Root disease, and Scab. Papaya fruits face threats from Anthracnose, Black spot, Phytopthora, powdery mildew and ring spot. while Apple fruits are vulnerable to blotch, rot, and scab which can harm productivity and quality if not addressed early.
References
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