Detection of Cotton Plant Diseases Using Deep Transfer Learning

Authors

  • Vani Rajasekar Department of CSE, Kongu Engineering College, Perundurai, Erode, India https://orcid.org/0000-0003-1851-6526
  • K Venu Department of CSE, Kongu Engineering College, Perundurai, Erode, India https://orcid.org/0000-0003-1851-6526
  • Soumya Ranjan Jena Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology, India https://orcid.org/0000-0003-1099-5649
  • R. Janani Varthini Department of CSE, Kongu Engineering College, Perundurai, Erode, India
  • S. Ishwarya Department of CSE, Kongu Engineering College, Perundurai, Erode, India

DOI:

https://doi.org/10.13052/jmm1550-4646.1828

Keywords:

Xception, ResNet, ImageNet, Convolutional Neural Network (CNN), Deep CNN.

Abstract

Agriculture is a vital part of every country’s economy, and India is regarded an agro-based nation. One of the main purposes of agriculture is to yield healthy crops without any disease. Cotton is a significant crop in India in relation to income. India is the world’s largest producer of cotton. Cotton crops are affected when leaves fall off early or become afflicted with diseases. Farmers and planting experts, on the other hand, have faced numerous concerns and ongoing agricultural obstacles for millennia, including much cotton disease. Because severe cotton disease can result in no grain harvest, a rapid, efficient, less expensive and reliable approach for detecting cotton illnesses is widely wanted in the agricultural information area. Deep learning method is used to solve the issue because it will perform exceptionally well in image processing and classification problems. The network was built using a combination of the benefits of both the ResNet pre-trained on ImageNet and the Xception component, and this technique outperforms other state-of-the-art techniques. Every convolution layer with in dense block is tiny, so each convolution kernel is still in charge of learning the tiniest details. The deep convolution neural networks for the detection of plant leaf diseases contemplate utilising a pre-trained model acquired from usual enormous datasets, and then applying it to a specific task educated with their own data. The experimental results show that for ResNet-50, a training accuracy of 0.95 and validation accuracy of 0.98 is obtained whereas training loss of 0.33 and validation loss of 0.5.

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Author Biographies

Vani Rajasekar, Department of CSE, Kongu Engineering College, Perundurai, Erode, India

Vani Rajasekar completed B. Tech (Information Technology), M. Tech (Information and Cyber warfare) in Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India. She is pursuing her Ph.D. (Information and Communication Engineering) in the area of Biometrics and Network security. Presently she is working as an Assistant professor in the Department of Computer Science and Engineering, Kongu Engineering College Erode, Tamil Nadu, India for the past 5 years. Her areas of interest include Cryptography, Biometrics, Network Security, and Wireless Networks. She has authored around 20 research papers and book chapters published in various international journals and conferences which were indexed in Scopus, Web of Science, and SCI.

K Venu , Department of CSE, Kongu Engineering College, Perundurai, Erode, India

K. Venu is currently working as Assistant Professor in the department of Computer Science & Engineering in Kongu Engineering College, Tamilnadu, India. She is pursuing Ph.D., in Machine Learning under Anna University. She has completed 5 years of teaching service. She has published 5 articles in International/National Conference. She has authored 1 book chapter with reputed publishers. She has published 3 articles in International Journals.

Soumya Ranjan Jena, Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology, India

Soumya Ranjan Jena is currently working as an Assistant Professor in the Department of CSE, School of Computing at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology, Avadi, Chennai, Tamil Nadu, India. He has teaching and research experience from various reputed institutions in India like Galgotias University, Greater Noida, Uttar Pradesh, AKS University, Satna, Madhya Pradesh, K L Deemed to be University, Guntur, Andhra Pradesh, GITA (Autonomous), Bhubaneswar, Odisha. He has been awarded M.Tech in Information Technology from Utkal University, Odisha, B.Tech in Computer Science & Engineering from BPUT, Odisha, and Cisco Certified Network Associate (CCNA) from Central Tool Room and Training Centre (CTTC), Bhubaneswar, Odisha. He has got the immense experience to teach to graduate as well as post-graduate students and author of three books i.e. “Mastering Disruptive Technologies- Applications of Cloud Computing, IoT, Blockchain, Artificial Intelligence and Machine Learning Techniques”, “Theory of Computation and Application” and “Design and Analysis of Algorithms”. Apart from that he has also published more than 25 research papers on Cloud Computing, IoT in various international journals and conferences which are indexed by Scopus, Web of Science, and also published six patents out of which one is granted in Australia.

R. Janani Varthini, Department of CSE, Kongu Engineering College, Perundurai, Erode, India

R. Janani Varthini is BE student of Computer Science and Engineering department of Kongu Engineering College. She graduated in the year 2021. Her area of interest includes Datamining, Machine learning and Deep learning.

S. Ishwarya, Department of CSE, Kongu Engineering College, Perundurai, Erode, India

S. Ishwarya is BE student of Computer Science and Engineering department of Kongu Engineering College. She graduated in the year 2021. Her area of interest includes Machine learning and Deep learning.

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Published

2021-11-16

Issue

Section

Computer Vision and its Application in Agriculture