Rice Science ›› 2023, Vol. 30 ›› Issue (3): 247-256.DOI: 10.1016/j.rsci.2023.03.008

• Research Paper • Previous Articles     Next Articles

Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

Zhou Longfei1, Meng Ran1,4(), Yu Xing2, Liao Yigui1, Huang Zehua1, Lü Zhengang1, Xu Binyuan1, Yang Guodong2, Peng Shaobing2, Xu Le2,3()   

  1. 1College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
    2National Key Laboratory of Crop Genetic Improvement / Hubei Hongshan Laboratory / Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, Ministry of Agriculture and Rural Affairs / College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
    3College of Agriculture, Northeast Agricultural University, Harbin 150006, China
    4Harbin Institute of Technology Institute for Artificial Intelligence Co., Ltd, Harbin 150000, China
  • Received:2022-08-20 Accepted:2022-11-30 Online:2023-05-28 Published:2023-03-13
  • Contact: Meng Ran (mengran@mail.hzau.edu.cn); Xu Le (Le.Xu@mail.hzau.edu.cn)


Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API (VIs & Tex + API) improved the accuracy of yield prediction than single-phase UAV imagery-based feature, with the panicle initiation stage being the best period for yield prediction (R2 as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi-temporal method (multi-temporal model VIs & Tex: R2 as 0.795, RMSE as 0.298, RRMSE as 0.072) can increase R2 by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.

Key words: ratoon rice, yield prediction, unmanned aerial vehicle, multi-temporal feature, agronomic practice, stubble height