The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network
Open accessAbstract
Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized the frames into video. In the still-image detector, we used faster-RCNN as the framework. We used image-training models to detect relatively blurry videos. Additionally, a set of video-based evaluation metrics based on a machine learning classifier was proposed, which reflected the quality of video detection effectively in the experiments. Experiments showed that our system with the custom backbone was more suitable for detection of the untrained rice videos than VGG16, ResNet-50, ResNet-101 backbone system and YOLOv3 with our experimental environment.
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Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
Hasan, R. I., Yusuf, S. M., & Alzubaidi, L. (2020) · Plants · doi:10.3390/plants9101302 · Cited by 213
A YOLO-Based Pest Detection System for Precision Agriculture
Lippi, M., Bonucci, N., Carpio, R. F., Contarini, M., Speranza, S., & Gasparri, A. (2021) · 2021 29th Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med51440.2021.9480344 · Cited by 109
Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning
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Metadata
- Authors
- Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., Wang, F., Zhou, M., & Liu, W.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-01-21
- Volume
- 20
- Issue
- 3
- Pages
- 578
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3003067960
- Cited by
- 253
- References
- 39
- FWCI
- 22.39
- Type
- journal article
- Language
- en
- Open access
- gold
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- Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., Wang, F., Zhou, M., & Liu, W. (2020). A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network. Sensors. https://doi.org/10.3390/s20030578
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