College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310008, China.
Shandong Academy of Agricultural Machinery Science, Jinan 250100, China.
Sensors (Basel). 2023 Nov 10;23(22):9107. doi: 10.3390/s23229107.
Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25-0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R = 0.979, R = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R = 0.977, R = 0.981, RPD = 7.222) and effective phosphorus (R = 0.969, R = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.
土壤肥力对茶树的生长至关重要。土壤的理化性质在土壤肥力评价中起着关键作用。因此，实现土壤理化性质的快速准确检测，对促进茶园精准农业的发展具有重要意义。近年来，光谱数据已成为土壤理化性质无损检测的重要手段。本研究构建了支持向量回归（SVR）模型，对不同粒径的茶园土壤水解氮、有效钾和有效磷进行建模。然后，采用成功投影算法（SPA）和最小角回归（LAR）和引导软收缩（BOSS）变量重要性筛选方法对土壤理化性质中的变量进行优化。结果表明，粒径为 0.25-0.5mm 的土壤对三种理化性质的预测效果最好。进一步采用降维方法后，LAR 算法（R = 0.979、R = 0.976、RPD = 6.613）在粒径为 0.25~0.5 的水解氮预测模型中表现最佳。使用数据降维方法和 BOSS 方法估计有效钾（R = 0.977、R = 0.981、RPD = 7.222）和有效磷（R = 0.969、R = 0.964、RPD = 5.163）的模型具有最佳的准确性。为了为茶园土壤理化性质的准确检测提供参考，本研究研究了不同土壤粒径下各理化性质的建模效果，并将回归模型与各种降尺度策略相结合。
