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A Hybrid Approach to Identify Plant Leaf Disease using Machine Learning and Image Processing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 27 |
| Year of Publication: 2023 |
| Authors: Prabhjot Kaur, Barinderjit Kaur |
10.5120/ijca2023923020
|
Prabhjot Kaur, Barinderjit Kaur . A Hybrid Approach to Identify Plant Leaf Disease using Machine Learning and Image Processing. International Journal of Computer Applications. 185, 27 ( Aug 2023), 25-30. DOI=10.5120/ijca2023923020
Abstract
One of the main factors affecting agricultural output decrease globally is plant disease and crop losses must be avoided through early diagnosis of these diseases.Machine learning (ML) and Image processing techniques have shown great potential in automating plant disease identification. The recent developments in the field of image processing and ML for plant leaf disease identification, include pre-processing, image acquisition,classification, andfeature extraction. Additionally, we provide a summary of the various ML algorithms utilized for plant leaf disease classification, including supervised, unsupervised, and deep learning algorithms. Furthermore, we discuss some of the challenges faced in plant leaf disease identification using MLand image processing techniques, such as the need for large-scale datasets having 256x256 size of images and the generalization of models across different plant species and environmental conditions. Machine learning algorithms can be used to learn from multiple sources of data. Fusion methods may be used to merge data from several sources to produce anML model that is more reliable and precise. To get better results, we will apply fusion techniques that can be utilized to improve the robustness and accuracy of plant leaf disease identification by combining multiple sources of information. Fusion techniques involve combining multiple sources of information, such as different types of images or features, to create a more comprehensive representation of the plant leaf and its disease.
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