ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION
The need for an ensemble classifier arises due to better accuracy; reduced overfitting, increased robustness which handles the noisy data and reduced variance of individual models, by combining the advantages and overcoming the drawbacks of the individual classifier. We have performed a comparison...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2024-12-01
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| Series: | Applied Computer Science |
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| Online Access: | https://ph.pollub.pl/index.php/acs/article/view/6479 |
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| author | Puja SARAF Jayantrao PATIL Rajnikant WAGH |
| author_facet | Puja SARAF Jayantrao PATIL Rajnikant WAGH |
| author_sort | Puja SARAF |
| collection | DOAJ |
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The need for an ensemble classifier arises due to better accuracy; reduced overfitting, increased robustness which handles the noisy data and reduced variance of individual models, by combining the advantages and overcoming the drawbacks of the individual classifier. We have performed a comparison of different classifiers like Support Vector Machine (SVM), XGBoost, Random Forest (RF), Naive Bayes (NB), Convolutional Neural Network (CNN) and proposed Ensemble method used in the classification task. Among all the classifiers evaluated, CNN was found to be the most accurate having an accuracy rate of 93.7%. This indicates that CNN can identify complex data patterns that are also important for photo recognition and classification tasks. Nonetheless, NB and SVM only achieved medium results with accuracy rates of 82.66% and 85.6% respectively. These could have been due to either the complexity of data being handled or underlying assumptions made. RF and XGBoost demonstrated remarkable performances by employing ensemble learning methods as well as gradient-boosting approaches with accuracies of 83.33% and 90.7% respectively. Our Ensemble method outstripped all individual models at an accuracy level of 95.5%, indicating that more than one technique is better when classifying correctly based on various resource allocations across techniques employed thereby improving such outcomes altogether by combining them. These results display the pros and cons of every classifier on the Plant Village dataset, giving vital data to improve plant disease classification and guide further research into precision farming and agricultural diagnostics.
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| format | Article |
| id | doaj-art-759a6230c76549529a4966605e61673c |
| institution | Kabale University |
| issn | 2353-6977 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj-art-759a6230c76549529a4966605e61673c2025-01-09T12:44:47ZengPolish Association for Knowledge PromotionApplied Computer Science2353-69772024-12-0120410.35784/acs-2024-38ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSIONPuja SARAF0https://orcid.org/0000-0002-2439-4783Jayantrao PATIL1https://orcid.org/0000-0002-9545-339XRajnikant WAGH2https://orcid.org/0000-0003-2997-6034Department of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, MaharashtraDepartment of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, MaharashtraDepartment of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra The need for an ensemble classifier arises due to better accuracy; reduced overfitting, increased robustness which handles the noisy data and reduced variance of individual models, by combining the advantages and overcoming the drawbacks of the individual classifier. We have performed a comparison of different classifiers like Support Vector Machine (SVM), XGBoost, Random Forest (RF), Naive Bayes (NB), Convolutional Neural Network (CNN) and proposed Ensemble method used in the classification task. Among all the classifiers evaluated, CNN was found to be the most accurate having an accuracy rate of 93.7%. This indicates that CNN can identify complex data patterns that are also important for photo recognition and classification tasks. Nonetheless, NB and SVM only achieved medium results with accuracy rates of 82.66% and 85.6% respectively. These could have been due to either the complexity of data being handled or underlying assumptions made. RF and XGBoost demonstrated remarkable performances by employing ensemble learning methods as well as gradient-boosting approaches with accuracies of 83.33% and 90.7% respectively. Our Ensemble method outstripped all individual models at an accuracy level of 95.5%, indicating that more than one technique is better when classifying correctly based on various resource allocations across techniques employed thereby improving such outcomes altogether by combining them. These results display the pros and cons of every classifier on the Plant Village dataset, giving vital data to improve plant disease classification and guide further research into precision farming and agricultural diagnostics. https://ph.pollub.pl/index.php/acs/article/view/6479Ensemble classifierLeaf DiseaseFeature FusionDeep LearningMachine Learning |
| spellingShingle | Puja SARAF Jayantrao PATIL Rajnikant WAGH ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION Applied Computer Science Ensemble classifier Leaf Disease Feature Fusion Deep Learning Machine Learning |
| title | ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION |
| title_full | ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION |
| title_fullStr | ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION |
| title_full_unstemmed | ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION |
| title_short | ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION |
| title_sort | enhancing tomato leaf disease detection through multimodal feature fusion |
| topic | Ensemble classifier Leaf Disease Feature Fusion Deep Learning Machine Learning |
| url | https://ph.pollub.pl/index.php/acs/article/view/6479 |
| work_keys_str_mv | AT pujasaraf enhancingtomatoleafdiseasedetectionthroughmultimodalfeaturefusion AT jayantraopatil enhancingtomatoleafdiseasedetectionthroughmultimodalfeaturefusion AT rajnikantwagh enhancingtomatoleafdiseasedetectionthroughmultimodalfeaturefusion |