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http://dx.doi.org/10.11627/jkise.2021.44.3.033

Performance Comparison of Base CNN Models in Transfer Learning for Crop Diseases Classification  

Yoon, Hyoup-Sang (Department of Software Convergence, Daegu Catholic University)
Jeong, Seok-Bong (School of Railway, Kyungil University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.44, no.3, 2021 , pp. 33-38 More about this Journal
Abstract
Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.
Keywords
Crop Disease; Transfer Learning; Convolutional Neural Network; Performance Evaluation;
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