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.

1. 서 론
2. 연구방법
    2.1 연구목적
    2.2 농작물 질병분류기 구조
    2.3 질병분류기의 학습 및 성능평가 방법
3. 성능 비교
    3.1 실험 환경 및 데이터
    3.2 학습된 농작물의 질병분류 정확도
    3.3 미학습된 신규 농작물의 질병진단 정확도
4. 결 론
References

• Hyoup-Sang Yoon(대구가톨릭대학교 소프트웨어융합학과) | 윤협상
• Seok-Bong Jeong(경일대학교 철도학부) | 정석봉 Corresponding Author