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Detection and Classification of Leaf Disease Using Deep Neural Network

Detection and Classification of Leaf Disease Using Deep Neural Network

Meeradevi, Monica R. Mundada, Shilpa M.
Copyright: © 2022 |Pages: 27
ISBN13: 9781799881612|ISBN10: 179988161X|ISBN13 Softcover: 9781799881629|EISBN13: 9781799881636
DOI: 10.4018/978-1-7998-8161-2.ch004
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MLA

Meeradevi, et al. "Detection and Classification of Leaf Disease Using Deep Neural Network." Deep Learning Applications for Cyber-Physical Systems, edited by Monica R. Mundada, et al., IGI Global, 2022, pp. 51-77. https://doi.org/10.4018/978-1-7998-8161-2.ch004

APA

Meeradevi, Mundada, M. R., & M., S. (2022). Detection and Classification of Leaf Disease Using Deep Neural Network. In M. Mundada, S. Seema, S. K.G., & M. Shilpa (Eds.), Deep Learning Applications for Cyber-Physical Systems (pp. 51-77). IGI Global. https://doi.org/10.4018/978-1-7998-8161-2.ch004

Chicago

Meeradevi, Monica R. Mundada, and Shilpa M. "Detection and Classification of Leaf Disease Using Deep Neural Network." In Deep Learning Applications for Cyber-Physical Systems, edited by Monica R. Mundada, et al., 51-77. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8161-2.ch004

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Abstract

Modern technologies have improved their application in field of agriculture in order to improve production. Plant diseases are harmful to plant growth, which leads to reduced quality and quantity of crop. Early identification of plant disease will reduce the loss of the crop productivity. So, it is necessary to identify and diagnose the disease at an early stage before it spreads to the entire field. In this chapter, the proposed model uses VGG16 with attention mechanism for leaf disease classification. This model makes use of convolution neural network which consist of convolution block, max pool layer, and fully connected layer with softmax as an activation function. The proposed approach integrates CNN with attention mechanism to focus more on the diseased part of leaf and increase the classification accuracy. The proposed model design is a novel deep learning model to perform the fine tuning in the classification of nine different type of tomato plant disease.

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