Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection
The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers t...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/1598796 |
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| _version_ | 1849690600231665664 |
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| author | Abu Sarwar Zamani L. Anand Kantilal Pitambar Rane P. Prabhu Ahmed Mateen Buttar Harikumar Pallathadka Abhishek Raghuvanshi Betty Nokobi Dugbakie |
| author_facet | Abu Sarwar Zamani L. Anand Kantilal Pitambar Rane P. Prabhu Ahmed Mateen Buttar Harikumar Pallathadka Abhishek Raghuvanshi Betty Nokobi Dugbakie |
| author_sort | Abu Sarwar Zamani |
| collection | DOAJ |
| description | The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3. |
| format | Article |
| id | doaj-art-eb5f67da26a3462880ce4605af25a2af |
| institution | DOAJ |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-eb5f67da26a3462880ce4605af25a2af2025-08-20T03:21:16ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/1598796Performance of Machine Learning and Image Processing in Plant Leaf Disease DetectionAbu Sarwar Zamani0L. Anand1Kantilal Pitambar Rane2P. Prabhu3Ahmed Mateen Buttar4Harikumar Pallathadka5Abhishek Raghuvanshi6Betty Nokobi Dugbakie7Department of Computer and Self DevelopmentDepartment of Networking and CommunicationsDepartment of Electronics and CommunicationAlagappa UniversityDepartment of Computer ScienceManipur International UniversityMahakal Institute of TechnologyDepartment of Chemical EngineeringThe aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.http://dx.doi.org/10.1155/2022/1598796 |
| spellingShingle | Abu Sarwar Zamani L. Anand Kantilal Pitambar Rane P. Prabhu Ahmed Mateen Buttar Harikumar Pallathadka Abhishek Raghuvanshi Betty Nokobi Dugbakie Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection Journal of Food Quality |
| title | Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection |
| title_full | Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection |
| title_fullStr | Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection |
| title_full_unstemmed | Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection |
| title_short | Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection |
| title_sort | performance of machine learning and image processing in plant leaf disease detection |
| url | http://dx.doi.org/10.1155/2022/1598796 |
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