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Soil Testing Using Image Processing

International Journal of Electronics and Communication Engineering
© 2025 by SSRG - IJECE Journal
Volume 12 Issue 4
Year of Publication : 2025
Authors : Shivani Sisodia, Saurabh Dhyani
10.14445/23488549/IJECE-V12I4P101
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How to Cite?

Shivani Sisodia, Saurabh Dhyani, "Soil Testing Using Image Processing," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 4, pp. 1-7, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I4P101

Abstract:

Precision agriculture is a globally growing practice that requires accurate soil health assessment. The health of the soil is characterized by parameters such as pH and nutrients like NPK. Soil testing is very important to predict crop yield and manage fertilizers and pesticides. Traditionally, soil testing methods were time and resource-intensive, often leading to delays. This discouraged farmers from conducting regular soil tests. We propose a machine learning-based application for rapid soil analysis. We have utilized 7000 soil images paired with their corresponding laboratory-tested results. These samples were used to train our CNN (Convolutional Neural Network) model to identify soil properties from images. The machine learning model ensures accuracy and robustness as it has been trained under various lighting conditions. Preliminary evaluations indicate an average prediction variance of 0.02 pH units, 1.5 kg/ha for N, 0.8 kg/ha for P, and 1.2kg/ha for K measured as the Mean Squared Error normalized by the range of actual nutrient values. This innovation aims to contribute to sustainable agricultural practices by making soil testing in real-time possible without the need for any extra equipment or expertise.

Keywords:

Feature extraction, Image processing, Pattern recognition, Remote sensing, Segmentation.

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