  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 1,133 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 (R^2 = 0.80) and OC
(R^2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data,
further improved predictions for available Mn and SAI with R^2 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.
