<![CDATA[Background: Plant diseases continue to threaten agricultural productivity worldwide, causing significant reductions in crop yield and quality. Traditional visual inspection by farmers or experts is often slow, subjective, and unreliable, especially across large plantation areas. With the increasing availability of digital imaging technologies, automated detection through image processing and machine learning presents a promising alternative.Aims: This study aims to develop an enhanced plant disease detection framework using image processing combined with machine learning algorithms, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN).Methods: A dataset of 54,306 leaf images from the PlantVillage collection was used to train and test the models. Preprocessing steps included resizing, noise removal, background segmentation, and feature extraction. CNNs were trained for end-to-end classification, while SVM operated on manually extracted features. A 10-fold cross-validation procedure was employed to ensure robustness. Fine-tuning strategies and comparative experiments were implemented to evaluate performance consistency across dataset variants.Result: The system demonstrated strong capability in early disease detection, achieving 97% accuracy for healthy leaves and moderate performance (56%) for certain diseased classes due to visual similarity and image noise. Background segmentation improved focus on disease features, while grayscale images reduced reliance on color cues but lowered classification accuracy.Conclusion: The findings confirm that machine learning, particularly CNN-based models, can significantly enhance plant disease diagnosis and support timely agricultural decision-making. Future improvements will explore advanced deep learning architectures, expanded datasets, multimodal imaging, and IoT integration for real-time field deployment.]]>
