Soil quality monitoring is important in precision agriculture.This study aimed to examine the possibility of assessing the soil parameters in apple-growing regions using spectroscopic methods.A total of 111 soil samples were collected from 11 typical sites of apple orchards,and the croplands surrounding them.Near-infrared(NIR) and mid-infrared(MIR) spectra,combined with partial least square regression,were used to predict the soil parameters,including organic matter(OM) content,pH,and the contents of As,Cu,Zn,Pb,and Cr.Organic matter and pH were closely correlated with As and the heavy metals.The NIR model showed a high prediction accuracy for the determination of OM,pH,and As,with correlation coefficients(r) of 0.89,0.89,and 0.90,respectively.The predictions of these three parameters by MIR showed reduced accuracy,with r values of 0.77,0.84,and 0.92,respectively.The heavy metals could also be measured by spectroscopy due to their correlation with organic matter.Both NIR and MIR had high correlation coefficients for the determination of Cu,Zn,and Cr,with standard errors of prediction of 2.95,10.48,and 9.49 mg kg-1 for NIR and 3.69,5.84,and 6.94 mg kg-1 for MIR,respectively.Pb content behaved differently from the other parameters.Both NIR and MIR underestimated Pb content,with r values of 0.67 and 0.56 and standard errors of prediction of 3.46 and 2.99,respectively.Cu and Zn had a higher correlation with OM and pH and were better predicted than Pb and Cr.Thus,NIR spectra could accurately predict several soil parameters,metallic and nonmetallic,simultaneously,and were more feasible than MIR in analyzing soil parameters in the study area. Soil quality monitoring is important in precision agriculture. This study aimed to examine the possibility of assessing the soil parameters in apple-growing regions using spectroscopic methods. A total of 111 soil samples were collected from 11 typical sites of apple orchards, and the croplands surrounding combined with partial least square regression, were used to predict the soil parameters, including organic matter (OM) content, pH, and contents of As, Cu, Zn, Pb, and Cr. Organic matter and pH were closely correlated with As and the heavy metals. NIR model showed a high prediction accuracy for the determination of OM, pH, and As, with correlation coefficients (r) of 0.89, 0.89 , and 0.90, respectively. These predictions of these three parameters by MIR showed reduced accuracy, with r values ​​of 0.77, 0.84, and 0.92, respectively. The heavy metals could also be measured by spectroscopy due to their correlation with organic matter. Both NIR and MIR had hi gh correlation coefficients for the determination of Cu, Zn, and Cr, with standard errors of prediction of 2.95,10.48, and 9.49 mg kg-1 for NIR and 3.69,5.84, and 6.94 mg kg-1 for MIR, respectively. Pb content behaved differently from the other parameters.Both NIR and MIR underestimated Pb content, with r values ​​of 0.67 and 0.56 and standard errors of prediction of 3.46 and 2.99, respectively. Cu and Zn had a higher correlation with OM and pH and were better predicted than Pb and Cr.Thus, NIR spectra could only predict several soil parameters, metallic and nonmetallic, simultaneously, and were more feasible than MIR in analyzing soil parameters in the study area.