The Effect of Sample Size and Data Numbering on Precision of Calibration Model to predict Soil Properties
Journal Title: Journal of Agricultural Machinery - Year 2017, Vol 7, Issue 2
Abstract
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 were increased from 0.46 to 0.72 and from 1.3 to 2.0 respectively. Finally, in categories based on PCA, the RMSE fell down almost linearly (0.19-0.13). Potassium prediction model with the least amount of R2 (0.48) and phosphorus with highest number of errors (RMSE = 5.28) were the weakest models in whole data. Other properties of the soil had a higher coefficient of determination (R2> 0.5). Therefore, prediction models had acceptable accuracy. At least 77, 105 and 105 samples are required for precise calibration model of OC, nitrogen and pH respectively. Due to the different conditions of farms, comparing these results with previous findings is very complex. Furthermore, model accuracy did not improve by increasing data of calibration models to the total number of samples. While in previous studies, more precise model was calibrated by considering the entire data sets. Among all factors of soil, acidity has little dependence on the other soil properties. The pH modeling is also confirmed by Moros (2009) however, the more error was reported here. There is no certain pH range in the NIR spectra, and usually it is distinguishable from the other properties of the soil (Kuang and Mouazen, 2011). Conclusion Spectroscopic methods exhibited good potential for detecting soil properties. MSC and BC can effectively remove irrelevant information to improve prediction accuracy. Using different methods to select numbers of data for the calibration models presented similar results, but in the meantime PCA technique provided the best answer. Supplementary, the ever-increasing number of data does not always improve modeling accuracy. It is better to choose numbers of data according to principal components (PCs) of PCA to obtain acceptable answer. It must be noted that every crops requires a specific soil and nutrients, so it is necessary to develop models for other soil properties.
Authors and Affiliations
H. Mohamadi-Monavar,
Keywords
Related Articles
- EP ID EP717872
- DOI -
- Views 51
- Downloads 0
Monitoring of Conservation Tillage and Tillage Intensity by Ground and Satellite Imagery
Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop re...
Pattern Recognition of Near-Infrared Spectroscopy for Non-Destructive Discrimination of Oranges Based on Taste Index
In recent years, application of near-infrared spectroscopy (NIR) as a non-destructive technique combined with chemometric methods has been widely noticed for quality assessment of food and agricultural products. In chemo...
Effect of Adding Sugarcane Bagasse and Filter Cake and Wetting and Drying Cycles on Pre-Compaction Stress of Soil
Introduction The compaction of soil by agricultural equipment has become a matter of increasing concern because compaction of arable lands may reduce crop growth and yield, and it also has environmental impacts. In natur...
Investigation of Energy Indices and Energy Consumption Optimization for Peach Production- Case Study: Saman Region in Chaharmahal va Bakhtiari Province
As one of the most important conditions in sustainable agriculture, optimization of energy consumption in agriculture is necessary in order to reduce the production cost and saving non renewable resources as well as redu...
Technical Evaluation of Three Methods of Manual, Semi-mechanized, and Mechanized Peanut Harvesting in Moghan
IntroductionThe harvesting stage is the most crucial phase in peanut production. In other words, one of the critical stages in producing this product is the harvest stage. Although it has its difficulties, this stage is...