A PSO-CNN-based approach for Enhancing Precision in Plant Leaf Disease Detection and Classification

Abstract

The Plant diseases that impact the leaves can hinder the progress of plant species, making earlier and more precise diagnoses crucial to minimize additional harm. However, the intriguing methods required additional time, expertise, and exclusivity. Utilizing leaf images for disease identification, research into deep learning (DL) holds significant promise for enhancing accuracy. The substantial progress in deep learning has opened up opportunities to enhance the precision and efficiency of plant leaf disease identification systems. This work introduces an innovative approach for plant disease detection and classification called Particle Swarm Optimization with Convolutional Neural Network (PSO-CNN). The work also explored disease categories in plant leaves using Particle Swarm Optimization (PSO), which extracts color, texture, and leaf arrangement information from images through a CNN classifier. Several effectiveness metrics were employed to evaluate and suggest that the presented approach outperforms existing techniques in terms of accuracy and performance measures, particularly during the stages of disease detection, including image acquisition, segmentation, noise reduction, and classification.