Wei Lai, Ding Yu, Chen Jing, Yang Linyu, Wei Jinyu, Shi Yinan, Ma Zigao, Wang Zhiying, Chen Wenjie, Zhao Xingqiang
Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China.
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China.
Front Chem. 2023 Jan 13;11:1123003. doi: 10.3389/fchem.2023.1123003. eCollection 2023.
Chemical fertilizers are important for effectively improving soil fertility, promoting crop growth, and increasing grain yield. Therefore, methods that can quickly and accurately measure the amount of fertilizer in the soil should be developed. In this study, 20 groups of soil samples were analyzed using laser-induced breakdown spectroscopy, and partial least squares (PLS) and random forest (RF) models were established. The prediction performances of the models for the chemical fertilizer content and pH were analyzed as well. The experimental results showed that the and root mean square error (RMSE) of the chemical fertilizer content in the soil obtained using the full-spectrum PLS model were .7852 and 2.2700 respectively. The predicted for soil pH was .7290, and RMSE was .2364. At the same time, the full-spectrum RF model showed of .9471 (an increase of 21%) and RMSE of .3021 (a decrease of 87%) for fertilizer content. for the soil pH under the RF model was .9517 (an increase of 31%), whereas RMSE was .0298 (a decrease of 87%). Therefore, the RF model showed better prediction performance than the PLS model. The results of this study show that the combination of laser-induced breakdown spectroscopy with RF algorithm is a feasible method for rapid determination of soil fertilizer content.
化肥对于有效提高土壤肥力、促进作物生长和增加粮食产量至关重要。因此，应开发能够快速准确测量土壤中肥料含量的方法。在本研究中，使用激光诱导击穿光谱法分析了20组土壤样品，并建立了偏最小二乘法（PLS）和随机森林（RF）模型。还分析了模型对化肥含量和pH值的预测性能。实验结果表明，使用全光谱PLS模型获得的土壤中化肥含量的决定系数（R²）和均方根误差（RMSE）分别为0.7852和2.2700。土壤pH值的预测R²为0.7290，RMSE为0.2364。同时，全光谱RF模型对肥料含量的R²为0.9471（增加了21%），RMSE为0.3021（减少了87%）。RF模型下土壤pH值的R²为0.9517（增加了31%），而RMSE为0.0298（减少了87%）。因此，RF模型显示出比PLS模型更好的预测性能。本研究结果表明，激光诱导击穿光谱法与RF算法相结合是快速测定土壤肥料含量的可行方法。
