농작물 질병분류를 위한 전이학습에 사용되는 기초 합성곱신경망 모델간 성능 비교

• 영문명: Performance Comparison of Base CNN Models in Transfer Learning for Crop Diseases Classification
• 발행기관: 한국산업경영시스템학회
• 저자명: 윤협상(Hyoup-Sang Yoon) 정석봉(Seok-Bong Jeong)
• 간행물 정보: 『산업경영시스템학회지』제44권 제3호, 33~38쪽, 전체 6쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.09.30

영문 초록

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.

목차

1. 서론

2. 연구방법

3. 성능 비교

4. 결론

키워드

Crop Disease Transfer Learning Convolutional Neural Network Performance Evaluation