Plant Disease Detection System for Agricultural Application in Cloud Using CNN - Raghavendran, S; Kumar, P; Silambarasan, K (International Journal of Computer Sciences and Engineering, 2018)
Plants are cultivated for food, medicine, clothing, shelter, fiber, and beauty for thousands of years. Fungi, bacteria, and viruses are the causing source of plant disease. So, need of an Automatic detection of plant disease for this problem. A traditional method of plant disease detection is not efficient and also unreliable. Due to pest attack, nearly 18% of crop yield is lost in worldwide during every year. Identification of plant disease is difficult in manually but which is a key factor to preventing the losses. In existing, a module is applied in a farm, that contains large number of different sensors and also a device is used for converting and transfer data for monitoring and controlling purposes. And then Image processing is showing the disease visually. In this, we approach a Convolutional Neural Network (CNN) classification model deployed in a smart phone app and also responsible to predict the plant disease for dynamic plants image. This method is generic and useful. Frequently, we should adding and updating new diseases in the datasets and then cloud computing is used for storing, retrieving and serving data. Captured image of normal plants are stored in the cloud server, and these images are compared with the diseased plant leaves in the cloud campus. This paper presents a automated detection of various diseases associated with crops and also given a proposed methodology for computing amount of diseases in various crops.