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학술대회 Crops Disease Diagnosing using Image-based Deep Learning Mechanism
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저자
박현, 은지숙, 김세한
발행일
201808
출처
International Conference on Computing and Network Communications (CoCoNet) 2018, pp.23-26
DOI
https://dx.doi.org/10.1109/CoCoNet.2018.8476914
협약과제
18HH3200, 스마트팜 확산을 위한 클라우드 기반 스마트베드 시스템 및 Farm-As-A-Service 기술 개발, 김세한
초록
To increase the crop productivity environmental factors or product resource, such as temperature, humidity, labor and electrical costs are important. However, above all, crop disease is the crucial factor and causes 20-30% reduction of the productivity in case of its infection. Thus, the disease of the crop is much more important factor affecting the productivity of the crops. Therefore, the farmer concentrates on the cause of the disease in the crops during its growth, but it is not easy to recognize the disease on the spot. Until now, they just relied on the opinion of the experts or their own experiences when the disease is doubtful. However, it triggers a decrease in productivity as no taking appropriate action and time. In this paper, to address this problem we provide the mechanism, which dynamically analyses the images of the disease. The analysis result is immediately sent to the farmer required the decision and then feedback from the farmer is reflected to the model. The mechanism performs the diagnosing of the disease, especially for the strawberry fruits and leaves, with data set of images using deep learning. Thus, it encourages increasing of the productivity through the fast recognition of disease and the consequent action.
키워드
CNN, Crops, Deep Learning, diagnosing, Image-based, Strawberry disease
KSP 제안 키워드
Crop disease, Crop productivity, Data sets, Environmental factor(E-factor), Image-based, deep learning(DL), learning mechanism