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@article{150789,
        author = {Hariharan K and Nandhini S and Kamali D and Dr. S. Saravanan},
        title = {Plant Disease Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {10},
        pages = {278-281},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150789},
        abstract = {Humans need to increase food production by an estimated 70% by 2050. Currently, infectious diseases reduce the potential yield by an average of 40% with many farmers in the developing world experiencing yield losses as high as 100%. Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way. It is very difficult to monitor the plant diseases manually. The widespread distribution of smartphones among crop growers around the world offers the potential of turning the smartphone into a valuable tool for diverse communities growing food. One potential application is the development of mobile disease diagnostics through machine learning and crowdsourcing.
In this proposed system, we provide a plant disease recognition using image processing and machine learning techniques. In particular, we concentrate on the use of RGB images owing to the low cost and high availability of smartphone cameras. We are planning to focus on the use of deep. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. We will be using Transfer learning technique using MobileNet pretrained on  ImageNet.},
        keywords = {},
        month = {},
        }