Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry

• URL: http://arxiv.org/abs/2404.12415v2
• Date: Thu, 5 Sep 2024 05:38:13 GMT
• Title: Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry
• Authors: Shubhadip Dasgupta, Satwik Pate, Divya Rathore, L. G. Divyanth, Ayan Das, Anshuman Nayak, Subhadip Dey, Asim Biswas, David C. Weindorf, Bin Li, Sergio Henrique Godinho Silva, Bruno Teixeira Ribeiro, Sanjay Srivastava, Somsubhra Chakraborty,
• Abstract summary: This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment. A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed.
• Score: 3.431158134976364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment, with a focus on key indicators such as available boron (B), organic carbon (OC), available manganese (Mn), available sulfur (S), and the sulfur availability index (SAI). A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed. The research integrated color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results showed that combining image features (IFs) with AVs significantly improved prediction accuracy for available B (R2 = 0.80) and OC (R2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further enhanced predictions for available Mn and SAI, with R2 values of 0.72 and 0.70, respectively. The study highlights the potential of integrating these technologies to offer rapid, cost-effective soil testing methods, paving the way for more advanced predictive models and a deeper understanding of soil fertility. Future work should explore the application of deep learning models on a larger dataset, incorporating soils from a wider range of agro-climatic zones under field conditions.

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