Agriculture is the backbone of each and every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Further, it contributes a significant amount to India’s Gross Domestic Product (GDP). So, protecting and enhancing the agricultural sector helps in developing India’s economy. In this work, a real-time decision support system integrated with camera sensor module is designed and developed for identification of plant disease. Further, the performance of three machine learning algorithm such as Extreme Learning Machine (ELM), Support Vector Machine (SVM) with linear and polynomial kernels is analyzed. Results demonstrate that the performance of extreme learning machine is better when compared to the adopted support vector machine classifier. Also, it is observed that the sensitivity of support vector machine with polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance since the developed real-time hardware is capable of detecting different plant diseases.