Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Xuchao Jiao, Hui Liu (), Weimu Wang, Jiaojiao Zhu and Hao Wang
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Xuchao Jiao: College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
Hui Liu: College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
Weimu Wang: College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
Jiaojiao Zhu: College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
Hao Wang: College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China

Agriculture, 2024, vol. 14, issue 6, 1-21

Abstract: Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R 2 value of 0.87 and RMSE of 6.61 g·kg −1 . For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R 2 of 0.77 and RMSE of 0.37 g·kg −1 . For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R 2 of 0.69 and RMSE of 0.04 g·kg −1 . For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R 2 of 0.83 and RMSE of 24.12 mg·kg −1 . The results demonstrate the high accuracy of these models in predicting soil nutrient content.

Keywords: hyperspectral; soil nutrient; partial least squares regression; multiple stepwise regression (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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