Efficient deep learning framework for arecanut disease detection using graph neural network and Bat algorithm.
Shadrach FD, Devasenapathy D, Anitha R, Kumar ML, Siripuri K.
Crop Improvement
Arecanut palms supply the betel nut used in products consumed by hundreds of millions of people across Asia, and faster disease detection could mean more stable harvests and lower prices.
Scientists built a computer vision system that can look at images of arecanut palm plants and spot signs of disease automatically. The system uses a type of AI that is especially good at recognizing complex patterns, combined with a search method inspired by how bats use echolocation to find things efficiently. This combination helps the AI find diseases faster and more accurately than older methods.
Key Findings
The model combines graph neural networks with a Bat algorithm optimizer to improve disease classification performance on arecanut crops
The deep learning framework is designed for efficient detection, suggesting reduced computational cost compared to standard approaches
The system targets arecanut (Areca catechu), a commercially significant palm crop widely cultivated across South and Southeast Asia
chevron_right Technical Summary
Researchers developed an AI system combining graph neural networks and a bat-inspired optimization algorithm to automatically detect diseases in arecanut (betel nut) palms with high accuracy, potentially helping farmers catch infections early before they spread.
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