Mulberry Leaf Disease Detection using Deep Learning
D. Deva Hema1, Sougata Dey2, Krishabh3, Anubhav Saha4

1D.Deva Hema*, Assistant Professor in SRM Institute of Science and Technology in Ramapuram, Chennai, (Tamil Nadu), India.
2Sougata Dey, B. Tech Degree in Computer Science and Engineering from SRM Institute of Science and Technology, (Tamil Nadu), India.
3Krishabh,  B.Tech Computer Science Engineering Student at SRM institute of Science and Technology, (Tamil Nadu), India.
4Anubhav Saha, B.Tech  Computer Science and Engineering in SRM Institiute of Science and Technology, Ramapuram, (Tamil Nadu), India.
Manuscript received on September 17, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3366-3371 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1521109119/2019©BEIESP | DOI: 10.35940/ijeat.A1521.109119
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Abstract: Disease diagnosis and classification in a mulberry plant using deep learning is an interesting technique which can be useful for farmers and researchers to identify and classify diseases. It helps to manage plant pathogens within fields effectively and automatically at a minimal cost. Major mulberry diseases usually express their symptoms on leaf area at the early stage of infection. Infections can be analysed and classified by processing the image using a computer or machine using different algorithms to interpret the information. This paper gives us a brief knowledge of mulberry leaf diseases which is used for automatic detection of disease. It presents in detail that the algorithm and techniques which are involved in classification based on different criteria for image segmentation. Our goal is to develop a more suitable deep algorithm for our task. These convolutional layers are mostly used for image processing. The system identifies and classify mulberry leaf diseases effectively with complex scenarios from the affected areas using CNN.
Keywords: Mulberry diseases, Convolutional neural network, Leaf Spots, Powdery Mildew.