Enhancing tomato leaf disease detection through multimodal feature fusion

• Autorzy: Saraf Puja , Patil Jayantrao , Wagh Rajnikant

Identyfikatory

DOI

10.35784/acs-2024-38

• Języki publikacji: EN

Abstrakty

EN

The need for an ensemble classifier is driven by better accuracy; reduced overfitting, increased robustness that copes with noisy data and reduced variance of individual models, combining the advantages and overcoming the drawbacks of the individual classifier. 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 was conducted. 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. The Ensemble method presented in this paper outperformed 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.

Słowa kluczowe

EN

leaf diseases; feature fusion; Deep Learning; Machine Learning; ensemble classifier

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 4
• Strony: 14--38
• Opis fizyczny: Bibliogr. 27 poz., fig., tab.

Twórcy

autor

Saraf Puja

• R.C. Patel Institute of Technology, Department of Computer Engineering, India

• https://orcid.org/0000-0002-2439-4783

autor

Patil Jayantrao

• R.C. Patel Institute of Technology, Department of Computer Engineering, India

• https://orcid.org/0000-0002-9545-339X

autor

Wagh Rajnikant

• R.C. Patel Institute of Technology, Department of Computer Engineering, India

• https://orcid.org/0000-0003-2997-6034

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).