TY - GEN
T1 - Comparative Analysis of Deep Learning Models for Cotton Leaf Disease Detection
AU - Mary, X. Anitha
AU - Raimond, Kumudha
AU - Raj, A. Peniel Winifred
AU - Johnson, I.
AU - Popov, Vladimir
AU - Vijay, S. J.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Cotton is the most essential crop and plays an important role in the agricultural economy of the country. Cotton crop is prone to many diseases because of changes in the climatic conditions, insects such as pink bollworm, and many other factors. These diseases decrease crop productivity, and at present farmers, are diagnosing the diseases with their own experience. But these kinds of naked-eye observations do not give accurate results on large plantation areas. Therefore, it is necessary to develop an automatic, accurate, and economic system for detecting leaf diseases. The aim of this work is to detect the infected cotton leaf using Convolutional Neural Network (ConvNet/CNN) which is a deep learning technique. Nearly 519 healthy leaves and 387 diseased leaves are collected from reliable sources and studied. This work focusses on the performance evaluation and comparison of the powerful CNN architectures: AlexNet, InceptionV3, and Residual Network (ResNet) 50, VGG 16, NASNet and Xception in detecting the diseased cotton leaf. Out of these six models, ResNet50 and VGG 16 has shown significant performance with 97.56% of accuracy.
AB - Cotton is the most essential crop and plays an important role in the agricultural economy of the country. Cotton crop is prone to many diseases because of changes in the climatic conditions, insects such as pink bollworm, and many other factors. These diseases decrease crop productivity, and at present farmers, are diagnosing the diseases with their own experience. But these kinds of naked-eye observations do not give accurate results on large plantation areas. Therefore, it is necessary to develop an automatic, accurate, and economic system for detecting leaf diseases. The aim of this work is to detect the infected cotton leaf using Convolutional Neural Network (ConvNet/CNN) which is a deep learning technique. Nearly 519 healthy leaves and 387 diseased leaves are collected from reliable sources and studied. This work focusses on the performance evaluation and comparison of the powerful CNN architectures: AlexNet, InceptionV3, and Residual Network (ResNet) 50, VGG 16, NASNet and Xception in detecting the diseased cotton leaf. Out of these six models, ResNet50 and VGG 16 has shown significant performance with 97.56% of accuracy.
KW - Convolutional neural network
KW - Cotton leaf disease
KW - Deep learning
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85135840562&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-2177-3_77
DO - 10.1007/978-981-19-2177-3_77
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AN - SCOPUS:85135840562
SN - 9789811921766
T3 - Lecture Notes in Electrical Engineering
SP - 825
EP - 834
BT - Disruptive Technologies for Big Data and Cloud Applications - Proceedings of ICBDCC 2021
A2 - Peter, J. Dinesh
A2 - Fernandes, Steven Lawrence
A2 - Alavi, Amir H.
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Big Data and Cloud Computing, ICBDCC 2021
Y2 - 20 August 2021 through 21 August 2021
ER -
