Support Vector Machine (SVM) for Tomato Leaf Disease Detection

<p>Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection.</p>