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Conference Paper
Crops Disease Diagnosing using Image-based Deep Learning Mechanism
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- Authors
- Hyeon Park, Eun JeeSook, Se-Han Kim
- Issue Date
- 2018-08
- Citation
- International Conference on Computing and Network Communications (CoCoNet) 2018, pp.23-26
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Crop disease, Crop productivity, Data sets, Environmental factor(E-factor), Image-based, deep learning(DL), learning mechanism