Plant disease identification is the most important sector in agriculture. Turmeric is one of the most important rhizomatous crops grown in India. The aim of this is to design, implement and evaluate an image processing based software solution for automatic detection and classification of turmeric plant leaf diseases. Providing fast, automatic, cheap and accurate image processing based solutions for that task can be great realistic significance. The turmeric leaf is highly exposed to disease like rhizome rot, leaf spot and leaf blotch. The identification of plant diseases requires close monitoring and hence this project adopts technologies to manage turmeric plant disease caused by fungi to enable production of high quality crop yields. Normally growth rate of plants is identified by measuring the height of the plant with regular intervals, but in this work it is proposed that growth rate can also be predicted based on leaf size and color. Hence leaf features are used for the same task. Color and texture features were incorporated for the identification of the leaf. A digital camera is used to acquire the disease leaf image and sent it to server by Android phone and then image processing of disease image is carried out by the server. The proposed approach consists of four phases such as preprocessing, segmentation, feature extraction and classification. The preprocessing phase involves image transformation and resize. In order to extract leaf shape and boundary features, leaf has to be segmented. Hence watershed algorithm is used for segmentation. The leaf images textural analysis was carried out using GLCM. SVM classifier is used to classify the feature extracted images after ranking their attributes using an information gain algorithm.