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http://dx.doi.org/10.9717/kmms.2020.23.10.1250

Tomato Crop Disease Classification Using an Ensemble Approach Based on a Deep Neural Network  

Kim, Min-Ki (Dept. of Computer Engineering, Gyeongsang National University Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.23, no.10, 2020 , pp. 1250-1257 More about this Journal
Abstract
The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.
Keywords
Crop Disease Classification; Ensemble Approach; Deep Neural Network;
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Times Cited By KSCI : 2  (Citation Analysis)
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