An Intelligent Image Processing and Machine Learning Model for Early Plant Leaf Disease Detection

Abstract

Plant leaf diseases are a major challenge to global food production, often leading to reduced crop yield and quality. Early and accurate identification of such diseases is crucial for effective crop management and sustainable agriculture. Traditional manual inspection methods are time-consuming, error-prone, and require expert knowledge, whereas existing automated methods often suffer from limited accuracy due to variations in lighting, background noise, and disease similarity. This paper proposes an adaptive machine learning approach for image-based plant leaf disease identification with enhanced performance. The framework employs advanced preprocessing techniques for noise reduction and feature enhancement, followed by a deep learning-based classification model that dynamically adapts to varying image conditions. Transfer learning and data augmentation strategies are incorporated to improve model generalization across diverse crop types and disease categories. Experimental results on benchmark plant disease datasets demonstrate that the proposed method achieves higher accuracy, precision, and robustness compared to conventional models. The adaptability of the framework allows it to be deployed in real-time agricultural settings, offering a scalable solution for precision farming and food security.