ADVANCED METHODS FOR DISEASE IDENTIFICATION IN FRUITS: A COMPREHENSIVE REVIEW OF COMPUTER VISION, SPECTROSCOPY AND MACHINE LEARNING TECHNIQUES

Abstract

Fruit diseases cause significant economic losses in global agriculture, with post-harvest losses exceeding 30% in developing countries. This paper reviews 28 advanced methods for early and accurate disease detection in fruits, comparing their efficacy across different agricultural contexts. We analyze: (1) computer vision systems achieving 95-98% accuracy using deep learning, (2) hyperspectral imaging detecting pre-symptomatic infections (400-1000nm range), and (3) portable biosensors for field deployment. A meta-analysis of 62 datasets reveals convolutional neural networks (CNNs) outperform traditional methods by 23% mean accuracy. The review highlights emerging trends in edge-AI devices and discusses implementation challenges, including dataset biases and environmental variability. We conclude with a decision framework for method selection based on crop type, infrastructure, and disease characteristics.