Plant diseases pose a serious threat to global food security by directly affecting agricultural production. Traditional expert observation-based diagnosis processes are time-consuming, subjective, and error-prone, making early and accurate diagnosis difficult. This has necessitated the development of image processing and artificial intelligence-based systems that can automatically recognize disease symptoms from leaf images. This study aims to automatically classify plant leaf diseases using the PlantDoc dataset, which consists of images collected under real-world conditions. First, various image processing steps, such as denoising, color space transformations, segmentation, and contour detection, were applied to the leaf images to extract color, texture, and geometry-based features. The resulting features were classified using Support Vector Machines, Random Forests, and k-Nearest Neighbors, and the performance of these models was compared. Furthermore, a deep learning-based MobileNetV2 model was trained using transfer learning and data augmentation techniques and compared with classical methods. Experimental results show that the Random Forests model achieved the highest accuracy rate among classical methods, at 81.5%, while the MobileNetV2 model outperformed all other methods, with an accuracy rate of 86.9%. These findings demonstrate that deep learning-based approaches have higher generalization capabilities on complex, multi-class real-world data. Furthermore, classical methods, thanks to their interpretability and low computational cost, can be a good alternative in resource-limited systems.
Merve Ozkan (Tue,) studied this question.