Information Processing in Agriculture (Jun 2020)
Soil texture classification using multi class support vector machine
Abstract
The objective of this study is to process the soil images to generate a digital soil classification system for rural farmers at low cost. Soil texture is the main factor to be considered before doing cultivation. It affects the crop selection and regulates the water transmission property. The conventional hydrometer method determines the percentage of sand, silt, and clay present in a soil sample. This method is very cost and time-consuming process. In this approach, we collect 50 soil samples from the different region of west Guwahati, Assam, India. The samples are photographed under a constant light condition using an Android mobile of 13 MP cameras. The fraction of sand, silt, and clay of the soil samples are determined using the hydrometer test. The result of the hydrometer test is processed with the United State Department of Agriculture soil classification triangle for the final soil classification. Soil images are processed through the different stages like pre-processing of soil images for image enhancement, extracting the region of interest for segmentation and the texture analysis for feature vector. The feature vector is calculated from the Hue, Saturation, and Value (HSV) histogram, color moments, color auto Correlogram, Gabor wavelets, and discrete wavelet transform. Finally, Support Vector Machine classifier is used to classify the soil images using linear kernel. The proposed method gives an average of 91.37% accuracy for all the soil samples and the result is nearly the same with the United State Department of Agriculture soil classification.
