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A Survey on Plant Leaf Disease Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 17 |
| Year of Publication: 2022 |
| Authors: Tannu Kumari, M.K. Jayanthi Kannan, Vinutha N. |
10.5120/ijca2022922170
|
Tannu Kumari, M.K. Jayanthi Kannan, Vinutha N. . A Survey on Plant Leaf Disease Detection. International Journal of Computer Applications. 184, 17 ( Jun 2022), 23-30. DOI=10.5120/ijca2022922170
Abstract
Climate change and pests’ attacks have a high impact on the healthy harvest in our country. A farmer can ensure the healthy production of his labor if he accurately and timely detects plant diseases. Earlier, disease detection was a manual job and it was impractical also. Now with the advancement of technology various Machine Leaning and Deep Leaning technologies are on a verge of replacing manual labor for leaf disease detection. Plant leaf disease detection using leaf images has now become a hot topic for many researchers. Here we have analyzed the model’s efficiency in various research papers which has incorporated Machine learning (ML) or Deep Learning (DL) technique. Some papers used only Image segmentation for leaf disease detection but these models were quite inefficient and inaccurate but managed to differentiate infected parts from healthy leaves. However, Image segmentation in combination with either machine learning or deep learning models improves leaf disease detection. Image segmentation helps in segmenting the infected part of the leaf from large data set and then feeds this result to aMachine Learning or Deep Learning model. The large data set usually has high noise. Hence, prepossessing also plays a vital role in the prediction of disease.
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