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Challenges and Issues in Plant Disease Detection Using Deep Learning

Challenges and Issues in Plant Disease Detection Using Deep Learning

Priyanka Sahu, Anuradha Chug, Amit Prakash Singh, Dinesh Singh, Ravinder Pal Singh
Copyright: © 2021 |Pages: 19
ISBN13: 9781799832997|ISBN10: 1799832996|ISBN13 Softcover: 9781799833000|EISBN13: 9781799833017
DOI: 10.4018/978-1-7998-3299-7.ch004
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MLA

Sahu, Priyanka, et al. "Challenges and Issues in Plant Disease Detection Using Deep Learning." Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, edited by Mohit Dua and Ankit Kumar Jain, IGI Global, 2021, pp. 56-74. https://doi.org/10.4018/978-1-7998-3299-7.ch004

APA

Sahu, P., Chug, A., Singh, A. P., Singh, D., & Singh, R. P. (2021). Challenges and Issues in Plant Disease Detection Using Deep Learning. In M. Dua & A. Jain (Eds.), Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security (pp. 56-74). IGI Global. https://doi.org/10.4018/978-1-7998-3299-7.ch004

Chicago

Sahu, Priyanka, et al. "Challenges and Issues in Plant Disease Detection Using Deep Learning." In Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, edited by Mohit Dua and Ankit Kumar Jain, 56-74. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3299-7.ch004

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Abstract

Deep learning (DL) has rapidly become an essential tool for image classification tasks. This technique is now being deployed to the tasks of classifying and detecting plant diseases. The encouraging results achieved with this methodology hide many problems that are rarely addressed in related experiments. This study examines the main factors influencing the efficiency of deep neural networks for plant disease detection. The challenges discussed in the study are based on the literature as well as experiments conducted using an image database, which contains approximately 1,296 leaf images of the beans crop. A pre-trained convolutional neural network, EfficientNet B0, is used for training and testing purposes. This study gives and emphasizes on factors and challenges that may potentially affect the use of DL techniques to detect and classify plant diseases. Some solutions are also suggested that may overcome these problems.

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