Hybrid Approaches for Plant Disease Recognition

Abstract

Plant diseases pose a significant threat to agriculture, leading to yield and quality losses. Traditional manual methods for disease identification are time-consuming and often yield inaccurate results. Automated systems leveraging image processing and machine learning techniques have emerged to improve accuracy and efficiency. Integrating these approaches allows image preprocessing and feature extraction to be combined with machine learning algorithms for pattern recognition and classification. Deep learning, particularly convolutional neural networks (CNNs), has revolutionized computer vision tasks, enabling hierarchical feature extraction. Hybrid methods offer advantages such as improved accuracy, faster identification, cost reduction, and increased agricultural productivity. This survey explores the significance and potential of hybrid approaches in plant disease identification, addressing the growing need for early detection and management in agriculture.