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Agriculture maintains a key role in India, because it provides food to the human beings even there is a drastically increasing number of populations. So in the field of agriculture, plant disease detection techniques are used. But human eyes are not able to detect these leaf diseases exactly. Therefore machine learning and image processing techniques are used for disease detection with specialized algorithm accurately. Image classification and analysis techniques are implemented for detecting leaf diseases. In this paper a modern approach for detection of leaf diseases using Image processing and Machine learning (ML) based Support Vector Machine (SVM) classifier is analyzed. By using digital cameras leaf images are collected from the agricultural fields and background removal, filtering, enhancement are the different techniques which are preprocessed on images. K-means clustering process is used for segmentation on colour based, which can detect the disease effecting leaf. Statistical Gray-Level Co-Occurrence Matrix (GLCM) features are used in feature extraction with the help of image segmentation. SVM classifier is used in appropriate feature section as image texture and colour. Experimental results can be shown that the accuracy of leaf disease detection using this modern approach is better than other state-of –art- techniques.