Deep-Learning-Based Rice Phenological Stage Recognition

Jiale Qin, Tianci Hu, Jianghao Yuan, Qingzhi Liu, Wensheng Wang, Jie Liu, Leifeng Guo, Guozhu Song*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to their changes, having guiding significance for agricultural production activities. Traditionally, crop phenological stages are determined mainly by manual analysis of remote sensing data collected by UAVs, which is time-consuming, labor-intensive, and may lead to data loss. To cope with this problem, this paper proposes a deep-learning-based method for rice phenological stage recognition. Firstly, we use a weather station equipped with RGB cameras to collect image data of the whole life cycle of rice and build a dataset. Secondly, we use object detection technology to clean the dataset and divide it into six subsets. Finally, we use ResNet-50 as the backbone network to extract spatial feature information from image data and achieve accurate recognition of six rice phenological stages, including seedling, tillering, booting jointing, heading flowering, grain filling, and maturity. Compared with the existing solutions, our method guarantees long-term, continuous, and accurate phenology monitoring. The experimental results show that our method can achieve an accuracy of around 87.33%, providing a new research direction for crop phenological stage recognition.

Original languageEnglish
Article number2891
Number of pages14
JournalRemote Sensing
Issue number11
Publication statusPublished - 1 Jun 2023


  • deep learning
  • phenology
  • ResNet
  • weather stations
  • Yolov5


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