Current Developments in Crop Disease Identification Using Computer Vision: A Critical Review of Related Works and Techniques

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 9

Year of Publication : 2024

Authors : Anandraddi Naduvinamani, Jayshri Rudagi, Mallikarjun Anandhalli

10.14445/23488549/IJECE-V11I9P116

How to Cite?

Anandraddi Naduvinamani, Jayshri Rudagi, Mallikarjun Anandhalli, "Current Developments in Crop Disease Identification Using Computer Vision: A Critical Review of Related Works and Techniques," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 9, pp. 174-191, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I9P116

Abstract:

Computer vision and especially deep learning play a pivotal role in the recognition of patterns in diverse kinds of images. Recent years have seen increased attention from both academic and business communities for autonomous learning in this field. Deep Learning (DL) based strategies have found much usage in areas like NLP, speech recognition, and video analysis. At the same time, it has become a centre of attraction for scientists studying methods for agricultural plant protection, such as pest spread assessments or the identification of plant diseases. Researchers may avoid the pitfalls of selecting disease spot qualities subjectively, derive more accurate plant disease features, and speed up the rate of technological transformation, all by disease detection in plants using deep learning. In this study, we will take a look at how far the DL mechanism has come in the arena of detecting ailments in crop leaves during the past few years. In this article, we converse about the cutting-edge progressions in the automatic diagnosis of leaf diseases, utilizing both deep learning and standard image processing methods, as well as highlight the obstacles that must be overcome.

Keywords:

Deep Learning (DL), Natural language processing, Disease spot qualities, Identification of plant diseases.

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