Leaf disease identification and classification using optimized deep learning
Diseases that affect plant leaves stop the growth of their individual species. Early and accurate diagnosis of plant diseases may reduce the likelihood that the plant will suffer further harm. The intriguing approach needed more time, exclusivity, and skill. Images of leaves are used to identify pla...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S266591742200277X |
| _version_ | 1811176109012680704 |
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| author | Yousef Methkal Abd Algani Orlando Juan Marquez Caro Liz Maribel Robladillo Bravo Chamandeep Kaur Mohammed Saleh Al Ansari B. Kiran Bala |
| author_facet | Yousef Methkal Abd Algani Orlando Juan Marquez Caro Liz Maribel Robladillo Bravo Chamandeep Kaur Mohammed Saleh Al Ansari B. Kiran Bala |
| author_sort | Yousef Methkal Abd Algani |
| collection | DOAJ |
| description | Diseases that affect plant leaves stop the growth of their individual species. Early and accurate diagnosis of plant diseases may reduce the likelihood that the plant will suffer further harm. The intriguing approach needed more time, exclusivity, and skill. Images of leaves are used to identify plant leaf diseases. Research on deep learning (DL) appears to have a lot of potential for improved accuracy. The substantial advancements and expansions in deep learning have created the opportunity to improve the coordination and accuracy of the system for identifying and appreciating plant leaf diseases. This study presents an innovative deep learning technique for disease detection and classification named Ant Colony Optimization with Convolution Neural Network (ACO-CNN).The effectiveness of disease diagnosis in plant leaves was investigated using ant colony optimization (ACO). Geometries of colour, texture, and plant leaf arrangement are subtracted from the provided images using the CNN classifier. A few of the effectiveness metrics used for analysis and proposing a suggested method prove that the proposed approach performs better than existing techniques with an accuracy rate concert measures are utilized for the execution of these approaches. These steps are used in the phases of disease detection: picture acquisition, image separation, nose removal, and classification. |
| first_indexed | 2024-04-10T19:47:48Z |
| format | Article |
| id | doaj.art-d6b718615d094c429c0b77c74cb1740a |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-10T19:47:48Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-d6b718615d094c429c0b77c74cb1740a2023-01-29T04:21:59ZengElsevierMeasurement: Sensors2665-91742023-02-0125100643Leaf disease identification and classification using optimized deep learningYousef Methkal Abd Algani0Orlando Juan Marquez Caro1Liz Maribel Robladillo Bravo2Chamandeep Kaur3Mohammed Saleh Al Ansari4B. Kiran Bala5Department of Mathematics, The Arab Academic College for Education in Israel-Haifa, Israel; Corresponding author.Universidad César Vallejo, PeruUniversidad César Vallejo, PeruDept of IT, Jazan University, Saudi ArabiaCollege of Engineering, Department of Chemical Engineering, University of Bahrain, BahrainDepartment of Artificial Intelligence and Data Science, K.Ramakrishnan College of Engineering, Trichy, Tamil Nadu, IndiaDiseases that affect plant leaves stop the growth of their individual species. Early and accurate diagnosis of plant diseases may reduce the likelihood that the plant will suffer further harm. The intriguing approach needed more time, exclusivity, and skill. Images of leaves are used to identify plant leaf diseases. Research on deep learning (DL) appears to have a lot of potential for improved accuracy. The substantial advancements and expansions in deep learning have created the opportunity to improve the coordination and accuracy of the system for identifying and appreciating plant leaf diseases. This study presents an innovative deep learning technique for disease detection and classification named Ant Colony Optimization with Convolution Neural Network (ACO-CNN).The effectiveness of disease diagnosis in plant leaves was investigated using ant colony optimization (ACO). Geometries of colour, texture, and plant leaf arrangement are subtracted from the provided images using the CNN classifier. A few of the effectiveness metrics used for analysis and proposing a suggested method prove that the proposed approach performs better than existing techniques with an accuracy rate concert measures are utilized for the execution of these approaches. These steps are used in the phases of disease detection: picture acquisition, image separation, nose removal, and classification.http://www.sciencedirect.com/science/article/pii/S266591742200277XPlant leaf diseaseAnt colony optimizationConvolution neural networkDisease detection |
| spellingShingle | Yousef Methkal Abd Algani Orlando Juan Marquez Caro Liz Maribel Robladillo Bravo Chamandeep Kaur Mohammed Saleh Al Ansari B. Kiran Bala Leaf disease identification and classification using optimized deep learning Measurement: Sensors Plant leaf disease Ant colony optimization Convolution neural network Disease detection |
| title | Leaf disease identification and classification using optimized deep learning |
| title_full | Leaf disease identification and classification using optimized deep learning |
| title_fullStr | Leaf disease identification and classification using optimized deep learning |
| title_full_unstemmed | Leaf disease identification and classification using optimized deep learning |
| title_short | Leaf disease identification and classification using optimized deep learning |
| title_sort | leaf disease identification and classification using optimized deep learning |
| topic | Plant leaf disease Ant colony optimization Convolution neural network Disease detection |
| url | http://www.sciencedirect.com/science/article/pii/S266591742200277X |
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