| Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
|---|---|---|
| Article Number | 01001 | |
| Number of page(s) | 14 | |
| Section | Electronics and Electical Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202339901001 | |
| Published online | 12 July 2023 | |
Machine Learning Based Automatic Leaf Diseases Detection
1 Assistant Professor, Department of ECE, KPRIET, Coimbatore
2,3,4,5 Student, Department of ECE, KPRIET, Coimbatore
* Corresponding author: prasad7research@gmail.com
The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and pre-processed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise and recallable in its ability to detect leaf illnesses, making it a potential method for practical use. This strategy may help with early leaf disease identification and prevention, increasing crop productivity and lowering the demand for toxic pesticides. Here we are identifying the Bacterial spot, Early blight.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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