Critical Review of Deep Learning Algorithms for Plant Diseases by Leaf Recognition

Authors

  • M. Jaithoon Bibi
  • Dr. S. Karpagavalli
  • A. Kalaivani

Keywords:

Crop pathology, Leaf diseases, Image processing, Deep learning, Disease classification

Abstract

The identification and classification of the crop leaf diseases plays an essential role in the cultivation. Plants are the livelihood. Peoples depend entirely on crops for the breathing of their daily lives. Thus, suitable crop caring should take place. Most research suggests that the quality of agricultural commodities can be restricted depending on different factors. Crop diseases include microorganisms and pathogens. The leaf diseases not only reduce crop growth, the cultivation is also destroyed. Several researchers have been identified crop leaf diseases using image processing algorithms but it take more time for detection. Therefore, advanced algorithms are required to identify and classify the crop leaf diseases automatically with higher accuracy. There are different deep learning algorithms using crop leaf images developed for automatically detecting the crop leaf diseases in an efficient manner. In this article, a survey on different deep learning algorithms using image processing for detecting and classifying the crop or plant leaf diseases is presented. Also, the merits and demerits of the surveyed algorithms for crop leaves diseases identification are addressed in a tabular form. Finally, a comprehensive analysis is concluded and future directions are suggested to increase the accuracy of leaf diseases classification.

References

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Jaithoon Bibi. M., &Karpagavalli, S. (2021).Positional-aware Dual Attention and Topology Fusion GAN for Plant Leaf Disease Image Super-resolution and Classification.LinguisticaAntverpiensia, 2021(3), ISSN :0304-2294 Pg.no:(12-25).

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Published

2021-10-30

How to Cite

Bibi, M. J. ., Karpagavalli, D. S., & Kalaivani, A. . (2021). Critical Review of Deep Learning Algorithms for Plant Diseases by Leaf Recognition. The Journal of Contemporary Issues in Business and Government, 27(5), 720–729. Retrieved from https://cibgp.com/au/index.php/1323-6903/article/view/2017

Issue

Vol. 27 No. 5 (2021): The journal of contemporary issues in business and government

Section

Articles

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