Solanaceae Safeguard: CNN-Swin Fusion for Precision Disease Management

Abstract

Agricultural productivity stands as a cornerstone of India's economy, and enhancing it remains a priority. A pivotal strategy in bolstering agricultural output is the timely identification of diseases. In agriculture, disease detection and management are crucial for ensuring crop health and yield. This study proposes a novel disease detection system for Solanaceae Vegetables utilizing a hybrid deep learning approach. The system integrates SWIN Transformer architecture with Convolutional Neural Networks (CNN) to analyze and classify disease patterns in Solanaceae vegetables. The dataset used for training and evaluation is sourced from Kaggle repository, comprising comprehensive images of diseased and healthy Solanaceae plants. Through extensive experimentation, the proposed hybrid model achieves a remarkable classification accuracy of 96%. The model demonstrated high precision, recall, and F1-scores across most classes, such as Class 0 (0.92, 0.89, 0.91) and Class 14 (0.97, 1.00, 0.99).The system's high accuracy demonstrates its potential as a reliable tool for disease detection and effective management strategies in Solanaceae vegetable cultivation, thereby contributing to enhanced leaf health and productivity.