Border Sensitive Knowledge Distillation for Rice Panicle Detection in UAV Images
Source abstract
Abstract
Research on panicle detection is one of the most important aspects of paddy phenotypic analysis. A phenotyping method that uses unmanned aerial vehicles can be an excellent alternative to field-based methods. Nevertheless, it... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (24)
Show source-provided references
1. Rice: Importance for global nutrition10.3177/jnsv.65.S2 ↗
2. Dash · Applications of Bioinformatics in Rice Research · 2021 · 17110.1007/978-981-16-3997-5_9 ↗
3. Analysis of rice panicle traits and detection of QTLs using an image analyzing method10.1270/jsbbs.60.55 ↗
4. Vishal · arXiv: 2004.02498 · 2020
5. Aerial monitoring of rice crop variables using an UAV robotic system
6. UAV T-YOLO-Rice: An enhanced tiny yolo networks for rice leaves diseases detection in paddy agronomy
7. DSC-T-Yolo-Rice: A sand clock yolo model for rice leaves diseases detection
8. Image analysis-based recognition and quantification of grain number per panicle in rice10.1186/s13007-019-0510-0 ↗
9. Yang · J. Exp. Bot. · Sep. 2015 · 66 · 560510.1093/jxb/erv100 ↗
10. An automatic approach for detecting seedlings per hill of machine-transplanted hybrid rice utilizing machine vision10.1016/j.compag.2021.106178 ↗
11. Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images10.1186/s13007-015-0047-9 ↗
12. A UAV open dataset of rice paddies for deep learning practice
13. Rice seedling detection in UAV images using transfer learning and machine learning10.3390/rs14122837 ↗
14. An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning10.1016/j.cj.2020.06.009 ↗
15. Qualifications of rice growth indicators optimized at different growth stages using unmanned aerial vehicle digital imagery
16. Field rice panicle detection and counting based on deep learning
17. Aerial imagery paddy seedlings inspection using deep learning10.3390/rs14020274 ↗
18. Universal detection of curved rice panicles in complex environments using aerial images and improved YOLOv4 model
19. Automated counting of rice panicle by applying deep learning model to images from unmanned aerial vehicle platform
20. Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images10.1016/j.fcr.2019.02.022 ↗
21. Locating and sizing method based on high-throughput UAV RGB images10.34133/plantphenomics.0020 ↗
22. Paddy rice imagery dataset for panicle segmentation10.3390/agronomy11081542 ↗
23. Object detection based on cortex hierarchical activation in border sensitive mechanism and classification-GIou joint representation10.1016/j.patcog.2022.109278 ↗
24. K. Chen et al., “MMDetection: Open MMLab detection toolbox and benchmark,” Jun. 17, 2019, arXiv.1906.07155.