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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.21 No.1
- 발행연도
- 2021.3
- 수록면
- 1 - 11 (11page)
- DOI
- 10.5391/IJFIS.2021.21.1.1
이용수
초록· 키워드
오류제보하기
In the article, the authors study the possibility of detecting some fungal diseases of rice using visual computing and machine learning techniques. Leaf blast and brown spot diseases are considered. Modern computer vision methods based on convolutional neural networks are used to identify a particular disease on an image. The authors compare the four most successful and compact convolutional neural network architectures: GoogleNet, ResNet-18, SqueezeNet-1.0, and DenseNet-121. The authors show that in the dataset used for the analysis, the disease can be detected with an accuracy of at least 95%. Testing the algorithm on real data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution. Data not used in training showed an accuracy of up to 95.6%. This is a good indicator of the reliability and stability of the obtained solution even to a change in the data distribution.
목차
Abstract
1. Introduction
2. Theory and Methods
3. Results
4. Discussion
5. Conclusion
References
참고문헌 (31)
참고문헌 신청
M. Shahbandeh / Rice Consumption Worldwide in 2019/2020 by country
G. L. Zelenskij / 2016 / Rice: biological basis of breeding and agricultural technology
Jayme Garcia Arnal Barbedo / 2016 / A review on the main challenges in automatic plant disease identification based on visible range images / Biosystems Engineering 144:52~60
V. K. Shrivastava / 2019 / Rice plant disease classification using transfer learning of deep convolutional neural network / International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6:631~635
J. P. Shah / 2016 / A survey on detection and classification of rice plant diseases / Proceedings of 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) :1~8
G. L. Zelenskij / 2016 / Rice: biological basis of breeding and agricultural technology
Jayme Garcia Arnal Barbedo / 2016 / A review on the main challenges in automatic plant disease identification based on visible range images / Biosystems Engineering 144:52~60
V. K. Shrivastava / 2019 / Rice plant disease classification using transfer learning of deep convolutional neural network / International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6:631~635
J. P. Shah / 2016 / A survey on detection and classification of rice plant diseases / Proceedings of 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC) :1~8
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