Deep Insights into Plant Health: Precision Plant Disease Detection Using Visual and Textual Feature Extraction Methods to Overcome Agricultural Barriers

Abstract

This project aims to improve crop health monitoring through an integrated visual and textual feature extraction approach for the accurate detection of leaf diseases. By leveraging the Internet of Everything (IoE), the system enables real-time connectivity between agricultural assets and a mobile phone application. Visual data from field cameras capture high-resolution images of crop leaves, which undergo preprocessing and segmentation. Convolutional Neural Networks (CNNs) are employed to extract visual features indicative of disease patterns. In parallel, textual data from agricultural databases, expert reports, and farmer testimonials are processed using Natural Language Processing (NLP) techniques to extract relevant textual features describing disease symptoms and environmental conditions. The extracted visual and textual features are fused using multimodal deep learning models, which are trained on labeled datasets to identify specific leaf diseases accurately. The models' predictions are then integrated into IoE-enabled decision support systems. This system facilitates early detection, enabling timely intervention and minimizing crop losses. Continuous monitoring and updates enhance the system's accuracy over time, benefiting sustainable agriculture practices.