Abstracts Earth and Environmental Sciences

Assessment of soil properties using microscope based computer vision

by Bharath Sudarsan

ABSTRACTSoil texture and organic matter content are important indicators of the quality and health of soil. They affect a range of soil properties and processes, which are fundamental for agriculture and civil engineering. Several traditional methods and advanced measurement techniques aim to address the challenge of quantifying these attributes. However, their cost, time requirements, sophisticated analytical methods and in-situ inapplicability pose a major challenge to rapid measurement. This research discloses the development of a new, inexpensive, microscope-based sensor system to estimate content of both sand and organic matter. The research was divided into two experiments conducted over a span of two years, each approaching the problem from two different computational perspectives. The first experiment involved images of air dried soil samples from Field 26 (acquired in 2014, organic soil) of the Macdonald Campus Farm of McGill University. The set of images was analyzed for sand and organic matter content using color and spatial image analysis, then validated against data obtained using conventional methods in a laboratory. Predictive relationships were developed using simple linear regressions based on parameters computed from the acquired imagery, such as hue, saturation, value, porosity, and variance estimates. The best sand and organic matter prediction models exhibited coefficients of determination (R2) values of 0.63 and 0.83, respectively, with RMSE = 84.7 g/kg for sand content and 0.11 for log SOM. In addition to the first set of images, the second experiment explored both laboratory and in situ measurements from Field 86 (acquired in 2015, mineral soil). This method used a continuous wavelet transform to characterize sand content which was in strong agreement with the laboratory measurements (r2 = 0.86 and RMSE = 44.7 g/kg for organic soil; r2 = 0.87 and RMSE = 40.2 g/kg for mineral soil). However, the efficiency of this algorithm was subpar for the images collected in-situ (r2 = 0.48 and RMSE = 80.6 g/kg). This was due to the excessive soil water content, which can be addressed by modifying the microscope holder design and data collection protocol. The portable nature of the image acquisition system and the good performance of the wavelet algorithm shows promise for the future use of the system to rapidly quantify key soil physical attributes. RÉSUMÉLa texture du sol et la teneur en matière organique sont des indicateurs importants de la qualité et de la santé du sol. Ils affectent un grand nombre de propriétés et de procédés du sol, plus précisément ils sont essentiels dans les secteurs de l'agriculture et du génie civil. En considérant ces derniers attributes, plusieurs processus traditionnels et techniques de prises de mesures avancées ont pour but de les quantifier. Toutefois, leurs coûts, leurs temps, les méthodes d'analyse sophistiquées et inadaptable en pratique in situ posent un défi majeur à la mesure rapide. Cette recherche révèle la conception et le développement d'un système de… Advisors/Committee Members: Viacheslav Adamchuk (Internal/Supervisor).