Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis

文献类型： 外文期刊

第一作者： Dong, Rui

作者： Dong, Rui;Miao, Yuxin;Wang, Xinbing;Yuan, Fei;Kusnierek, Krzysztof

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关键词： fluorescence sensing; nitrogen status; multiple linear regression; machine learning; precision nitrogen management

期刊名称：REMOTE SENSING （ 影响因子：5.349； 五年影响因子：5.786 ）

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年卷期： 2021 年 13 卷 24 期

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收录情况： SCI

摘要： Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex(R) 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R-2 = 0.73-0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R-2 = 0.46-0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R-2 = 0.84-0.93) and the most accurate diagnostic result.

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