A Hybrid Multi-class Classification Model for the Detection of Leaf Disease using XGBoost and SVM
A Hybrid Multi-class Classification Model for the Detection of Leaf Disease using XGBoost and SVM
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Rina Mahakud, Binod Kumar Pattanayak, Bibudhendu Pati
|DOI : 10.14445/22315381/IJETT-V70I10P229|
How to Cite?
Rina Mahakud, Binod Kumar Pattanayak, Bibudhendu Pati, "A Hybrid Multi-class Classification Model for the Detection of Leaf Disease using XGBoost and SVM," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 298-306, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P229
The primary source of human nutrition is generated from plants. Plants get affected by the disease, from crop farming to the production of foods. So, leaf disease identification is a crucial task in the farming industry. Various machine learning models are developed and evaluated by multiple researchers to identify leaf disease with significant results. This article compares the multi-class classification result of different state-of-art machine learning methods (SVM, LR, RF, KNN, DT, Extra Tree) with hybrid models. The model's performance is measured by precision, accuracy, F1 score, and confusion matrix. The experimentation shows that the hybrid model (MCLXGB) provides an impressive result of 93.22%, whereas the Decision Tree gives the least effective result of 74.57% accuracy.
Leaf Disease, Data Augmentation, Multi-Class Classification, XGBoost, Machine Learning.
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