An Approach Toward Classifying Plant-Leaf Diseases and Comparisons With the Conventional Classification

Authors

Anita Shrotriya, Manipal University Jaipur
Akhilesh Kumar Sharma, Manipal University Jaipur
Srikanth Prabhu, Manipal Institute of Technology
Amit Kumar Bairwa, Manipal University Jaipur

Document Type

Article

Publication Title

IEEE Access

Abstract

Plants play a crucial role in human history by providing essential resources such as food. However, they are susceptible to various diseases like leaf blight, grey spots, and rust, posing significant challenges for farmers and ranchers due to the potential damage and financial losses incurred. The current method of diagnosing plant diseases relies heavily on manual inspection by trained professionals, which becomes increasingly complex and resource-intensive, particularly on large farms. To address this issue, a novel approach is proposed, integrating clustering methods and neural networks to enhance the efficiency of identifying and studying plant disease samples. This method aims to expedite the diagnostic process and accurately quantify disease-induced damage. It introduces a formula for assessing damage, calculating the diseased leaf area as a fraction of the total leaf area. With an impressive accuracy rate of 96-97%, the system successfully identifies diseases such as Alternaria, Anthracnose, Bacterial Blight, and Cercospora Leaf Spot. This advancement signifies a notable improvement over previous methods, highlighting the evolution of the system. By harnessing the capabilities of clustering techniques and neural networks, this innovative approach seeks to revolutionize the detection and monitoring of plant diseases in agriculture, offering a more accessible and precise solution.

First Page

117379

Last Page

117398

DOI

10.1109/ACCESS.2024.3411013

Publication Date

1-1-2024

Recommended Citation

Shrotriya, Anita; Kumar Sharma, Akhilesh; Prabhu, Srikanth; and Kumar Bairwa, Amit, "An Approach Toward Classifying Plant-Leaf Diseases and Comparisons With the Conventional Classification" (2024). Open Access archive. 10966.
https://impressions.manipal.edu/open-access-archive/10966