Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Performance Comparison of Base CNN Models in Transfer Learning for Crop Diseases Classification

농작물 질병분류를 위한 전이학습에 사용되는 기초 합성곱신경망 모델간 성능 비교

  • 윤협상 (대구가톨릭대학교 소프트웨어융합학과) ;
  • 정석봉 (경일대학교 철도학부)
  • Received : 2021.07.22
  • Accepted : 2021.08.11
  • Published : 2021.09.30
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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.

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References

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