首页|Research on Machine Learning Discussed by Researchers at Tarim University (Predi cting Soil K+ and Na+ Contents in Cotton Field Using Machine Learning Algorithm)
Research on Machine Learning Discussed by Researchers at Tarim University (Predi cting Soil K+ and Na+ Contents in Cotton Field Using Machine Learning Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on artificial intelligence have been published. According to news reporting originating from Tarim Universi ty by NewsRx correspondents, research stated, “The contents of K+ and Na+ in soi l affect soil fertility and quality, and understanding their spatiotemporal chan ges and the factors influencing their changes is critical to improving soil mana gement and alleviating soil alkalization. We propose a machine learning method t o predict changes in K+ and Na+ content in soils.” Our news journalists obtained a quote from the research from Tarim University: “ Taking data measured from a cotton field in Southern Xinjiang as an example, we compared four machine learning algorithms: support vector regression (SVR), rand om forest regression (RFR), K-nearest neighbor regression (KNNR),and gradient l ifting regression tree (GBRT). All algorithms were first trained based on K+ and Na+ measured in 2020, and the trained models were then tested against the data measured in 2021. The accuracy and robustness of the models were evaluated using the mean absolute errors (MAE), root mean square error (RMSE), and the determin ation coefficient (R2). The MAE of SVR, RFR, KNNR and GBRT for predicting K+ con tent was 0.100, 0.169, 0.169 and 0.167 g/kg, respectively; their associated RMSE was 0.119, 0.218, 0.218 g/kg and 0.223 g/kg, respectively, and their R2 was 0.6 87, 0.437, 0.430, and 0.395, respectively. For predicting Na+ content, the MAE o f SVR, RFR, KNNR and GBRT was 0.841, 2.841, 2.826 g/kg, and 2.856 g/kg, respecti vely; and their RMSE was 1.154, 3.658, 3.630 g/kg, and 3.650 g/kg, respectively, and R2 was 0.838, 0.299, 0.219, and 0.200, respectively. SVR model is most accu rate for predicting soil K+ and Na+ in the depths of 0 10, 10 20, 20 30 and 30 4 0 cm, with its MAE for K+ at the four depths being 0.122, 0.114, 0.056 g/kg and 0.106 g/kg, respectively, and RMSE being 0.135, 0.135, 0.069 g/kg and 0.126 g/kg , respectively. The MAE of SVR for predicting Na+ at the four depths was 0.540, 0.619, 0.835 g/kg and 1.371 g/kg, respectively, and its RMSE was 0.636, 0.748, 1 .198 g/kg and 1.710 g/kg, respectively.”
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