Symptom-based early detection and classification of plant diseases using AI-driven CNN+KNN Fusion Software (ACKFS)

Abstract

This paper investigates and introduce an AI-driven CNN-KNN Fusion Software (ACKFS) for symptom-based early detection and classification of plant diseases. The approach integrates Convolutional Neural Networks and K-Nearest Neighbor’s to enhance classification accuracy. This research follows a structured four-phase process: pre-processing, segmentation, feature extraction, and classification. Using two datasets, ACKFS significantly improved accuracy to 94.56% and 87.52%, respectively. These results surpass the performance reported by previous researcher’s, demonstrating the effectiveness of CNN-KNN fusion for real-time plant disease detection on smart devices, contributing to precision agriculture and enhanced plant health monitoring.