ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION
Puja SARAF
pujasaraf20@gmail.comDepartment of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra (India)
https://orcid.org/0000-0002-2439-4783
Jayantrao PATIL
Department of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra (India)
https://orcid.org/0000-0002-9545-339X
Rajnikant WAGH
Department of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra (India)
https://orcid.org/0000-0003-2997-6034
Abstract
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.
Keywords:
Ensemble classifier, Leaf Disease, Feature Fusion, Deep Learning, Machine LearningReferences
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Authors
Puja SARAFpujasaraf20@gmail.com
Department of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra India
https://orcid.org/0000-0002-2439-4783
Authors
Jayantrao PATILDepartment of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra India
https://orcid.org/0000-0002-9545-339X
Authors
Rajnikant WAGHDepartment of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra India
https://orcid.org/0000-0003-2997-6034
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