The Effect of Sample Size and Data Numbering on Precision of Calibration Model to predict Soil Properties

Introduction Precision agriculture (PA) is a technology that measures and manages within-field variability, such as physical and chemical properties of soil. The nondestructive and rapid VIS-NIR technology detected a significant correlation between reflectance spectra and the physical and chemical properties of soil. On the other hand, quantitatively predict of soil factors such as nitrogen, carbon, cation exchange capacity and the amount of clay in precision farming is very important. The emphasis of this paper is comparing different techniques of choosing calibration samples such as randomly selected method, chemical data and also based on PCA. Since increasing the number of samples is usually time-consuming and costly, then in this study, the best sampling way -in available methods- was predicted for calibration models. In addition, the effect of sample size on the accuracy of the calibration and validation models was analyzed. Materials and Methods Two hundred and ten soil samples were collected from cultivated farm located in Avarzaman in Hamedan province, Iran. The crop rotation was mostly potato and wheat. Samples were collected from a depth of 20 cm above ground and passed through a 2 mm sieve and air dried at room temperature. Chemical analysis was performed in the soil science laboratory, faculty of agriculture engineering, Bu-ali Sina University, Hamadan, Iran. Two Spectrometer (AvaSpec-ULS 2048- UV-VIS) and (FT-NIR100N) were used to measure the spectral bands which cover the UV-Vis and NIR region (220-2200 nm). Each soil sample was uniformly tiled in a petri dish and was scanned 20 times. Then the pre-processing methods of multivariate scatter correction (MSC) and base line correction (BC) were applied on the raw signals using Unscrambler software. The samples were divided into two groups: one group for calibration 105 and the second group was used for validation. Each time, 15 samples were selected randomly and tested the accuracy of models, then other15 samples were added randomly to the previous set and it was done continuously. Finally, seven groups (15, 30... 105) were placed in each category. Results and Discussion All regression models on the whole pre-processed soil spectra were obtained in absorption mode. By increasing the number of samples in the calibration set of random group, RMSE was decreased from 0.2 to 0.13 nonlinearly. RMSE in the chemical test was also decreased almost linearly from 0.17 to 0.11. At the same time, R2 and RPD