Chinese Journal OF Rice Science >
Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images
Received date: 2021-07-29
Revised date: 2021-11-04
Online published: 2022-05-11
【Obiective】Leaf area index (LAI) is a crucial variable for assessing rice growth, and unmanned aerial vehicle (UAV) digital images can serve as an efficient way to real-time, no-destructive monitoring of crop growth parameters. However, it remains unclear which parameter in digital images can be used to estimate rice LAI. In addition, the optimal growth stage for monitoring is also unknown. 【Method】In this study, the UAV digital images were initially collected from two field experiments encompassing variations over two years with four cultivars at four nitrogen application levels. Then, the relationship between UAV digital image parameters (nine color indices and eight texture features) and rice LAI at different growth stages (tillering stage, jointing stage, booting stage, heading stage and filling stage) were analyzed. 【Result】The results suggested that the early growth stages, including both tillering stage and jointing stage, were suitable for rice LAI monitoring through UAV digital images, and the texture feature variance (VAR) exhibits greatest accuracy in model calibration with a determination coefficient (R2) of 0.7980. In the validation based on independent experiment, this texture feature also performs well with relative root mean square error (RRMSE) of 0.1658 and bias(θ) of 0.1306. 【Conclusion】Taking the accuracy and convenience in application into consideration, we found that the texture feature VAR could be used to monitor rice LAI in early growth stage with estimation models of LAI = 1.1656×exp(0.0174×VAR).
CAO Zhongsheng, LI Yanda, HUANG Junbao, YE Chun, SUN Binfeng, SHU Shifu, ZHU Yan, HE Yong . Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images[J]. Chinese Journal OF Rice Science, 2022 , 36(3) : 308 -317 . DOI: 10.16819/j.1001-7216.2022.210712
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