Disease Detection in Plant Leaf using LNet Based on Deep Learning

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors :   S. Bhuvaneswari, R. Surendiran, R. Aarthi, M. Thangamani, Rajasekhara babu Lingisetti
DOI : 10.14445/22315381/IJETT-V70I9P207

How to Cite?

S. Bhuvaneswari, R. Surendiran, R. Aarthi, M. Thangamani, Rajasekhara babu Lingisetti, "Disease Detection in Plant Leaf using LNet Based on Deep Learning ," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 64-75, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P207

Abstract
Deep Learning is a kind of artificial intelligence that uses fake brain organizations to learn how to learn. One of the most promising but difficult problems in leaf disease detection is the efficient and precise identification of diseases in Plant leaves. However, examining the leaves and determining the type of disease takes hours throughout the pasture. As a result, applying deep learning techniques and algorithms to identify fruit leaf disease has a lot of potential in modern agriculture. The research employs cutting-edge algorithms such as Xception, Inception V3 and ResNet-50, which produce good accuracy. The proposed architecture called Leaf Network (LNet) produces improved efficiency than the above listed Convolutional Neural Networks. It allows for the effective design of early plant leaf detection to reduce disease induced in crops during growth, harvest, and post-harvest processing, as well as to assure advanced disease diagnosis and prevention in crops. As a result, Leaf Network (LNet) has an accuracy of 98.68% compared with all four Algorithms.

Keywords
CNN, Xception, Inception V3, ResNet-50 and Leaf disease classification.

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