Prediction of Leaf Disease Using Image Processing Techniques: A Comprehensive Review
Abstract
The early detection and prediction of leaf diseases are crucial for minimizing crop losses and ensuring optimal agricultural productivity. Traditional methods of disease detection are often labor-intensive and time-consuming, highlighting the need for automated, efficient approaches. In recent years, image processing techniques, combined with machine learning algorithms, have emerged as powerful tools for diagnosing plant diseases. This review explores the various image processing methods used in the prediction of leaf diseases, including image segmentation, feature extraction, and classification techniques. The paper highlights the integration of machine learning, particularly deep learning models such as Convolutional Neural Networks (CNNs), with image processing to improve the accuracy and efficiency of disease detection. It also discusses the datasets used in research, such as the PlantVillage and PlantDoc datasets, and examines several case studies that demonstrate the real-world applications of these technologies in precision agriculture. While the application of image processing for leaf disease detection has shown promising results, challenges such as image quality variability, dataset limitations, and real-time implementation remain. The review concludes with an outlook on future developments, including the integration of image processing with IoT devices, mobile applications, and advanced AI models to further enhance disease detection capabilities.
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