AI Crop Disease Detection Using Mobile Camera
Robert Simon K1 ,Rohan Prakash2 , Sandip M3 , Mrs. Subbulakshmi4 , Mr. Arun.M5 , Dr. Senthilvelan.G6 1,2,3 Students, Department of Computer Science and Engineering
4Assistant Professor, Department of Artificial Intelligence
5,6 Assistant Professor, Department of Computer Science and Technology
Dr. M.G.R Educational and Research Institute, Chennai 600095, Tamil Nadu, India.
1 robert04simon@gmail.com 2 rohanprakash11a@gmail.com 3 imsandip92@gmail.com 4 subbulakshmi@drmgrdu.ac.in
5arun.cse@drmgrdu.ac.in 6senthilvelan.cse@drmgrdu.ac.in
Abstract-The paddy farming industry suffers a lot due to a number of diseases that are capable of producing a crop yield of 20-70 per cent. Conventional disease surveillance systems are slow, costly and need a person who is skilled and hence not accessible to small-scale farmers. The proposed AI-based system in this paper will involve the detection of the paddy disease through the remote sensing data of the Bhuvan and Bhoomi systems of the Indian Space Research Organization and the mobile camera. The given system uses a deep convolutional neural network (CNN) model that is mobile-oriented with an accuracy of 96.8 percent to recognize the major paddy diseases such as bacterial leaf blight, blast disease, brown spot, and sheath blight. The system is based on MobileNetV2 structure to perform efficient on-device inference with a mean processing time of 85ms per image. Combination with satellite Bhuvan imagery and Bhoomi land records allows monitoring of disease on a multi- scale; focusing on individual plants down to the area level. The process of field validation on 250 farmers confirmed the user satisfaction and the high rate of early disease detection increased considerably. The suggested system is a viable, economical, and accessible system of precision agriculture and food security. Index TermsPaddy disease detection, Mobile AI, Deep learn- ing, CNN, MobileNetV2, Remote sensing, Bhuvan, Bhoomi, Precision agriculture.
Keywords: Paddy Disease Detection, Mobile AI, Deep Learning, MobileNetV2, Precision Agriculture.
