‘MrudaTest’: An Android Phone Intervened Platform for Soil Primary Nutrients and pH Detection

ABSTRACT

‘Mruda’ is a Marathi term that signifies soil, and ‘MrudaTest’ shows soil testing. Crop yield is influenced by soil pH and macronutrients such as nitrogen (N), phosphorus (P), and potassium (K). Precision agriculture, which has gained appeal on a global scale, can be achieved in part by managing fertilizers properly in accordance with soil health. The standard laboratory test is time-consuming and resource-intensive. Sample testing takes longer duration due to overworked soil testing labs, which discourages farmers from performing soil fertility tests. To address the aforementioned issue, portable, farmer-friendly, rapid-testing technology is required, and our effort is geared in that direction. Soil samples were treated with reagents that turn the clear solution into color as per soil nutrients. An android phone was used to detect and quantify color information using a built-in camera, computing hardware, graphical user interface, image processing, and machine learning classification techniques. The machine learning model was trained using 410 samples collected under various lighting situations, followed by labeling and validation. On 100 samples, it was examined, and the estimated variance was determined to be between 2–11%. Additionally, we used unidentified soil samples to confirm our method, and we estimated an average variance of 8.9–11.7% for the stated nutrients, which is similar to the PUSA STFR kit data.

KEYWORDS:

• Android application
• image processing
• machine learning
• Ph
• soil fertility

Acknowledgements

This work was financially supported by Shri Ramdeobaba College of Engineering and Management, Nagpur under the Young Faculty Research Scheme. The authors wish to acknowledge concerned authorities for facilitating the lab infrastructure and funds.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The authors confirm that all relevant data are included in the article.