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딥러닝 기반 작물 질병 탐지 및 분류 시스템

Deep Learning-based system for plant disease detection and classification

  • 투고 : 2023.06.07
  • 심사 : 2023.07.14
  • 발행 : 2023.08.31

초록

작물의 병충해는 다양한 작물의 성장에 영향을 미치기 때문에 초기에 병충해를 식별하는 것이 매우 중요하다. 이미 많은 머신러닝(ML) 모델이 작물 병충해의 검사와 분류에 사용되었지만, 머신러닝의 부분 집합인 딥러닝(DL)이 발전을 이루면서 이 연구 분야에서 많은 진보가 있었다. 본 연구에서는 YOLOX 검출기와 MobileNet 분류기를 사용하여 비정상 작물의 병충해 검사 및 정상 작물에 대해서는 성숙도 분류를 진행하였다. 이 방법을 통해 다양한 작물 병충해 특징을 효과적으로 추출할 수 있으며, 실험을 위해 딸기, 고추, 토마토와 관련된 다양한 해상도의 이미지 데이터 셋을 준비하여 작물 병충해 분류에 사용하였다. 실험 결과에 따르면 복잡한 배경 조건을 가진 영상에서 평균 테스트 정확도가 84%, 성숙도 분류 정확도가 83.91% 임을 확인할 수 있었다. 이 모델은 자연 상태에서 3가지 작물에 대한 6가지 질병 검출 및 각 작물의 성숙도 분류를 효과적으로 진행할 수 있었다.

Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

키워드

과제정보

본 연구는 중소벤처기업부의 스마트서비스 ICT솔루션 개발사업의 일환으로 수행하였음. [RS-2022-00142483, AI 기반 원예질병 자동진단 및 처방지원 클라우드 서비스 솔루션]

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