Hybrid GNN- PDP Model for Leaf Disease Detection

Abstract

Introduction: As an important vegetable crop, cauliflower (Brassica oleracea var. botrytis) is afflicted by many diseases that significantly reduce yield and quality. Although advancements have taken place in agricultural technologies, a disease detection system based on deep learning yet remains to be developed for the specific purpose of cauliflower.

Objectives: The specific aim of this research is to develop an automated expert system that integrates the Internet of Things feature for cauliflower disease early detection with the use of deep learning. Such a system could facilitate farmers in identifying infections by employing mobile or portable-based devices to relay the results.

Methods:  The mobile and IoT-enabled devices have aided in the collection of 750 images of cauliflower plants. The Cat Swarm Optimization (CSO) technique was utilized to segment the affected areas visible in the given images. Feature extraction methods were explicitly engaged to acquire the statistical and co-occurrence features of the images. The study was limited to four diseases—in particular, bacterial soft rot, white rust, black rot, and downy mildew. The performance of the proposed model, termed GNN-PDP (Graph Neural Network-based Plant Disease Prediction), was observed against CNN, DNN, Random Forest, Decision Tree, LDA, and PCA classifiers.

Results: The GNN-PDP model achieved almost 89% accurate output, outperforming other models in disease classification.

Conclusion:  An effective solution for real-time detection of cauliflower diseases, allowing early intervention and promoting sustainable agriculture, is offered by the proposed GNN-based system.