Disease detection in plants is one of the essential steps in the field of agriculture to improve the quality and yield of crops. Applications of image processing play a major role in the early detection of diseases and also in terms of accuracy and time consumption. In many plant health monitoring systems, Fourier and wavelet transform is applied for feature extraction from plant images and then they are classified using different classifiers. In this study, tomato leaf images are collected from the PlantVillage database, images are preprocessed to improve the contrast, and then image segmentation is performed using the k-means clustering technique. Texture features are extracted from the region of interest using Discrete Wavelet Transforms (DWT). Fourteen image features obtained from Daubechies (db3), Symlet (sym3), and biorthogonal (Bior 3.3, Bior 3.5, Bior 3.7) wavelets. These features are used to classify the images as healthy and unhealthy with the help of the Support Vector Machine (SVM) classifier. Performance of the system is measured in terms of Sensitivity, Specificity, and Accuracy. The proposed system classifies the images with a sensitivity of 92%, specificity of 84%, and accuracy of 88%.