Correlation of pH on Soil Physicochemical Properties using Linear Regression Model for Spectral Data Analysis
Research Article- DOI: 10.23953/cloud.ijarsg.504
Keywords:soil; physicochemical properties; spectroscopy; pH
Soil properties may be varied by spatially and temporally with different agricultural practices. An accurate and reliable soil properties assessment is challenging issue in soil analysis. The soil properties assessment is very important for understanding the soil properties, nutrient management, influence of fertilizers and relation between soil properties which are affecting the plant growth. Conventional laboratory methods used to analyses soil properties are generally impractical because they are time-consuming, expensive and sometimes imprecise. On other hand, Visible and infrared spectroscopy can effectively characterize soil. Spectroscopic measurements are rapid, precise and inexpensive. Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. In the present research, we use spectroscopy techniques for soil properties analysis. The spectra of agglomerated farming soils were acquired by the ASD Field spec 4 spectroradiometer. Different fertilizers treatment applied soil samples are collected in pre monsoon and post monsoon season for 2 year (4 season) for banana and cotton crops in the form of DS-I and DS-II respectively. The soil spectra of VNIR region were preprocessed to get pure spectra. Then process the acquired spectral data by statistical methods for quantitative analysis of soil properties. The detected soil properties were carbon, Nitrogen, soil organic matter, pH, phosphorus, potassium, moisture sand, silt and clay. Soil pH is most important chemical properties that describe the relative acidity or alkalinity of the soil. It directly effect on plant growth and other soil properties. The relationship between pH properties on soil physical and chemical parameters and their influence were analyses by using linear regression model and show the performance of regression model with R2 and RMSE.
Keywords soil; physicochemical properties; spectroscopy; pH
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