Pest and Disease Identification in the Growth of Sweet Peppers using Faster R-CNN

被引:3
|
作者
Lin, Tu-Liang [1 ]
Chang, Hong-Yi [1 ]
Chen, Kai-Hong [1 ]
机构
[1] Natl Chiayi Univ, Dept Management Informat Syst, Taoyuan, Taiwan
关键词
D O I
10.1109/icce-tw46550.2019.8991893
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Plant pest control is very important, especially in the early stage. If the plant pests and diseases can be identified earlier, farmers can prevent them in advance and further avoid economic losses. Earlier identification of the pest and disease types can reduce the cost of pesticides. However, correct identification of pests and diseases requires knowledge and corresponding expertise, and this knowledge takes time to accumulate. Therefore, in this study, Faster R-CNN is adopted to develop a knowledge base system that can automatically identify plant pests and diseases.
引用
收藏
页数:2
相关论文
共 50 条
  • [1] The Pest and Disease Identification in the Growth of Sweet Peppers Using Faster R-CNN and Mask R-CNN
    Lin, Tu-Liang
    Chang, Hong-Yi
    Chen, Kai-Hong
    JOURNAL OF INTERNET TECHNOLOGY, 2020, 21 (02): : 605 - 614
  • [2] Defect Identification of Solar Panels Using Improved Faster R-CNN
    Zhang W.
    Ma Y.
    Bai X.
    Tan Y.
    Pi Y.
    Dianwang Jishu/Power System Technology, 2022, 46 (07): : 2593 - 2600
  • [3] Improved Faster R-CNN identification method for containers
    Chen, Ning
    Ding, Xiaohu
    Zhang, Hongyi
    INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS, 2020, 13 (03) : 308 - 317
  • [4] Lithology Identification Based on Improved Faster R-CNN
    Fu, Peng
    Wang, Jiyang
    MINERALS, 2024, 14 (09)
  • [5] Identification and Counting of Sugarcane Seedlings in the Field Using Improved Faster R-CNN
    Pan, Yuyun
    Zhu, Nengzhi
    Ding, Lu
    Li, Xiuhua
    Goh, Hui-Hwang
    Han, Chao
    Zhang, Muqing
    REMOTE SENSING, 2022, 14 (22)
  • [6] The comparison of Faster R-CNN and Atrous Faster R-CNN in different distance and light condition
    Srijakkot, K.
    Kanjanasurat, I.
    Wiriyakrieng, N.
    Benjangkaprasert, C.
    2019 12TH INTERNATIONAL CONFERENCE ON COMPUTER AND ELECTRICAL ENGINEERING, 2020, 1457
  • [7] Bolt Looseness Identification using Faster R-CNN and Grid Mask Augmentation
    Panmatharit, Atchapon
    Jiraraksopakun, Yuttapong
    Siripanichgorn, Anek
    Siricharoen, Punnarai
    PROCEEDINGS OF 2022 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC), 2022, : 1632 - 1637
  • [8] Pest Detection Based on Lightweight Locality-Aware Faster R-CNN
    Li, Kai-Run
    Duan, Li-Jun
    Deng, Yang-Jun
    Liu, Jin-Ling
    Long, Chen-Feng
    Zhu, Xing-Hui
    AGRONOMY-BASEL, 2024, 14 (10):
  • [9] Automatic dock identification based on improved Faster R-CNN
    Chang L.
    Wang X.
    Wang C.
    National Remote Sensing Bulletin, 2022, 26 (04) : 752 - 765
  • [10] Identification of the Genus of Stingless Bee Via Faster R-CNN
    Nizam, A.
    Mohd-Isa, W.
    Ali, A.
    INTERNATIONAL WORKSHOP ON ADVANCED IMAGE TECHNOLOGY (IWAIT) 2019, 2019, 11049