Enhancing Agricultural Disease Detection: A Multi-Model Deep Learning Novel Approach

Abstract

Artificial intelligence, especially deep learning, has attracted significant interest in bioinformatics, with prominent applications in precision agriculture. A significant threat to the agricultural sector is the rapid propagation of diseases from affected to healthy plants, which, if undetected, may culminate in significant crop losses. This research focusses on employing multi-model deep-learning techniques to identify diseases in the leaves of economically significant crops that are potatoes, tomatoes, grapes, apples, and peaches. These crops are widely grown and crucial for food security, with disease outbreaks threatening yield and quality. This study evaluates the performance of deep learning models, including VGG16, MobileNetV2, Xception, and ResNet, using four metrics, that is, Accuracy, Precision, Recall, and F1-Score. Furthermore, consumer research was undertaken to evaluate user trust in AI-driven multi-model systems, collecting feedback from farmers to inform future research directions. The results demonstrate that the VGG16 model outperformed all others in every evaluation criterion. Experimental simulations were performed in Jupyter Notebook utilizing Anaconda and Python. The findings indicate that the proposed multi-model approach allows a scalable, non-invasive, and contactless machine vision solution for the early detection of diseases in plant leaves, achieving an efficiency of 99% via multimodal classification techniques that incorporate statistical variables including mean, median, mode, skewness, and kurtosis.