Accelerated Diagnosis and Classification of Ash Gourd Leaf Diseases Using Vision Transformers

The Progresses in deep learning have transformed land disease, enabling more correct and effective crop health listening. This research presents a deep education-based categorization system for ruins fruit plant health analyst. The system promotes a changed DeepLabV3+ architecture to categorize plant representations into seven categories, containing healthy, differing mineral deficiencies, and fungal ailments. The classification model is prepared utilizing a curated dataset with dossier improving to enhance inference and accuracy. A Beaker-located API enables legitimate-time categorization, admitting users to transfer data to a server figure for immediate strength assessment. By mixing convolutional affecting animate nerve organs networks (CNNs) and transfer learning, this order provides a strong and adaptable solution for accuracy farming, empowering laborers and agricultural specialists to form informed conclusions for better crop management.