Microscope-based computer vision to characterize soil texture and soil organic matter

Abstract

Characterization and quantification of soil properties are important for the optimum use and management our soil. While Soil texture is an important factor for decision making in agriculture, civil engineering and other industries, soil organic matter (SOM) is the backbone of soil health and quality and also affects a range of other soil properties and processes. Traditional methods for estimating these soil properties are time consuming and laborious. This paper discloses the design and development of a new cost effective in situ computer vision-based sensor system to estimate soil texture and SOM. A small and inexpensive hand-held microscope was used to develop an image acquisition system. Images acquired in laboratory with variable texture and SOM were processed using means of geospatial data analysis based computer vision algorithm. Simple linear regression predictive relationships were developed to estimate soil texture and SOM using various computed parameters of acquired imagery. Predicted sand (coefficients of determination, R2 = 0.63) and SOM (R2 = 0.83) were in good agreement with the laboratory measurement. These correspond to root mean square error of 84.7 g sand kg−1 of soil and 0.11 log (SOM %) for soils exhibiting the range of % sand from 3.4 to 59.3 and % SOM from 5.5 to 72.8, respectively. Low cost and the portability of the acquisition system and the computer vision algorithm developed in this study suggested suitability for its use in both laboratory and field conditions and shows promise as a proximal soil sensor.