Soil fertility prediction using combined USB-microscope based soil image, auxiliary variables, and portable X-ray fluorescence spectrometry
Shubhadip Dasgupta, Satwik Pate, Divya Rathore, L. G. Divyanth, Ayan Kumar Das, Anshuman Nayak, Subhadip Dey, Asim Biswas, David C. Weindorf, Bin Li, Sérgio Henrique Godinho Silva, Bruno Teixeira Ribeiro, S. K. Srivastava, Somsubhra Chakraborty
Abstract
This study explored the application of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis to rapidly assess soil fertility, focusing on critical parameters such as available B, organic carbon (OC), available Mn, available S, and the sulfur availability index (SAI). Analyzing 1133 soil samples from various agro-climatic zones in Eastern India, the research combined color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results indicated that integrating image features (IFs) with auxiliary variables (AVs) significantly enhanced prediction accuracy for available B (R² = 0.80) and OC (R² = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further improved predictions for available Mn and SAI with R² values of 0.72 and 0.70, respectively. The study demonstrated how these integrated technologies have the potential to provide quick and affordable options for soil testing, opening up access to more sophisticated prediction models and a better comprehension of the fertility and health of the soil. Future research should focus on the application of deep learning models on a larger dataset of soil images, developed using soils from a broader range of agro-climatic zones under field condition.