지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제17권 제6호
- 발행연도
- 2021.12
- 수록면
- 1,115 - 1,126 (12page)
- DOI
- 10.3745/JIPS.04.0228
이용수
초록· 키워드
오류제보하기
Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent diseaseintrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.
목차
등록된 정보가 없습니다.
참고문헌 (30)
참고문헌 신청
Food and Agriculture Organization of the United Nations / New standards to curb the global spread of plant pests and diseases
Muqadas Bin Tahir / 2021 / WITHDRAWN: Recognition of Apple Leaf Diseases using Deep Learning and Variances-Controlled Features Reduction / Microprocessors and Microsystems :104027
S. Chakraborty / 2021 / Prediction of apple leaf diseases using multiclass support vector machine / Proceedings of 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), :147~151
S. R. N. M. Ayyub / 2019 / Fruit disease classification and identification using image processing / Proceedings of 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) :754~758
Ginne M. James / 2021 / Categorising Apple Fruit Diseases Employing Hybrid Neural Clustering Classifier / Materials Today: Proceedings
Muqadas Bin Tahir / 2021 / WITHDRAWN: Recognition of Apple Leaf Diseases using Deep Learning and Variances-Controlled Features Reduction / Microprocessors and Microsystems :104027
S. Chakraborty / 2021 / Prediction of apple leaf diseases using multiclass support vector machine / Proceedings of 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), :147~151
S. R. N. M. Ayyub / 2019 / Fruit disease classification and identification using image processing / Proceedings of 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) :754~758
Ginne M. James / 2021 / Categorising Apple Fruit Diseases Employing Hybrid Neural Clustering Classifier / Materials Today: Proceedings
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