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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 13, ISSUE 7, JULY 2024

A Survey on the Plant Leaf Disease Detection Techniques

Pramod M, Prasanna N H, Rohan N V , Tanush R , Dr.Ravikumar A V

DOI: 10.17148/IJARCCE.2024.13711

Abstract: Agricultural productivity is something on which the economy highly depends. This is one of the reasons that disease detection in plants plays an important role in the agricultural field. Having a disease in plants is quite natural. If proper care is not taken in this area then it causes serious effects on plants due to which respective product quality, quantity, or productivity is affected. For instance, little leaf disease is a hazardous disease found in (chili, banana, potato, and tomato) plants. Detection of plant disease through some automatic technique is beneficial as it reduces the large work of monitoring big farms of crops, Employing automated techniques for detecting plant diseases is beneficial, as it greatly reduces the extensive labour required to monitor large agricultural fields. The leaf diseases often manifest their symptoms on the leaf area during the early stages of infection. These infections can be analyzed and classified automatically using computer vision and machine vision systems, which employ image-processing techniques to interpret the information. The paper gives a brief review of major plant diseases that show symptoms in leaves and explains in details by involving image processing techniques.

Keywords: Leaf disease, Digital image processing, Color classification, Pattern classification.

How to Cite:

[1] Pramod M, Prasanna N H, Rohan N V , Tanush R , Dr.Ravikumar A V, “A Survey on the Plant Leaf Disease Detection Techniques,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2024.13711