21/08/2026
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๐ซ๐จ๐ฆ ๐๐ง ๐๐ง๐๐๐ซ๐ ๐ซ๐๐๐ฎ๐๐ญ๐ ๐
๐ข๐ง๐๐ฅ-๐๐๐๐ซ ๐๐ซ๐จ๐ฃ๐๐๐ญ ๐ญ๐จ ๐๐๐๐ ๐๐๐๐๐ฌ๐ฌ
Incredibly proud to announce that our research at the Multidisciplinary AI Research Centre, University of Peradeniya, has been accepted for publication in IEEE Access (SCImago Q1; Impact Factor: 4.2)!
This milestone is especially meaningful as it originated as an undergraduate Final Year Project, showcasing the immense value of cross-disciplinary collaboration between the Department of Electrical & Electronic Engineering and the Department of Civil Engineering at the University of Peradeniya.
๐ฑ Soil texture (clay, silt, and sand composition) is fundamental for agriculture and geotechnical engineering. However, conventional lab tests like sieve and hydrometer analyses are labor-intensive and often take 1 to 2 days to complete.
๐ก To create a faster, accessible alternative, we developed a custom, low-cost reflectance multispectral imaging system capturing 13 narrow spectral bands across the near-ultraviolet, visible, and near-infrared regions (365โ940 nm).
By combining this imaging data with advanced machine-learning models, our framework successfully delivers a rapid, non-destructive method for:
1. Direct classification of soil into the 12 USDA texture classes
2. Regression to accurately estimate clay, silt, and sand percentages
3. Indirect classification by mapping estimated composition onto the USDA soil texture triangle
A huge congratulations to the brilliant team and supervisors who made this multidisciplinary research a reality!
๐ฅ Team Members:
Sandunika Ranasinghe
Senith Jayakody
Mario De Silva
Gayathri Thilakarathne
๐ Supervisors:
Prof. Roshan Godaliyadda
Prof. Vijitha Herath
Prof. Parakrama Ekanayake
Dr. Sinniah K. Navaratnarajah
๐ Read the full open-access paper here: https://doi.org/10.1109/ACCESS.2026.3721276
