AI-Based Detection of Plant Diseases Using Image Processing Techniques

Abstract

Plant diseases pose a significant threat to global agricultural productivity, affecting both crop quality and yield. Early and accurate detection of plant diseases is essential to minimize economic losses and ensure food security. This study explores the application of artificial intelligence (AI) and image processing techniques for automated plant disease detection. The research outlines a comprehensive workflow involving image acquisition, preprocessing, feature extraction, segmentation, and classification. Key features such as color, texture, and shape are analyzed using both traditional machine learning methods and advanced deep learning models, particularly convolutional neural networks (CNNs). The study also highlights the importance of datasets, including the widely used PlantVillage dataset, and discusses challenges such as data quality, model generalization, and environmental variability. Case studies on crops like tomato and cereals demonstrate the effectiveness of AI-based approaches in identifying diseases at early stages. Despite promising advancements, issues related to dataset bias, interpretability, and real-world deployment remain critical. The paper concludes that integrating image processing with AI provides a powerful, scalable, and efficient solution for plant disease detection, with significant implications for precision agriculture and sustainable farming practices.