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Related Experiment Video

Updated: Jun 10, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Quantifying Crop Health Through Soil Moisture and Soil Quality Dynamics.

Adarsh Garg¹, Ankit Singh¹

¹GL Bajaj Institute of Management and Research, Management, Greater Noida, India.

Recent Advances in Food, Nutrition & Agriculture

|June 9, 2026

Summary

A new mathematical model quantifies crop health using soil moisture and quality. This tool helps farmers make informed decisions for better crop growth and productivity.

Keywords:

Crop health Crop yield Mathematical modeling Precision agriculture Smart farming Soil moisture Soil quality Sustainable agriculture.

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Area of Science:

Agricultural Science
Environmental Science
Data Science

Background:

Crop health is crucial for agricultural productivity.
Quantifying crop health across developmental stages is essential for sustained yield improvement.
Soil moisture and quality are key determinants of crop health.

Purpose of the Study:

To develop and validate a mathematical model for quantifying crop health.
To integrate soil moisture and soil quality indicators into a composite model.
To assess crop health dynamics throughout the crop lifecycle.

Main Methods:

A composite mathematical model integrating soil moisture and soil quality was developed.
Data was collected and the model was evaluated using a digital platform.
Model validation was performed against the Normalized Difference Vegetation Index (NDVI) scale through three iterations.

Main Results:

The model accurately quantifies crop health, providing insights into soil conditions.
Iteration 2 yielded a crop health value of 0.424, and Iteration 3 yielded 0.448.
These values fall within the moderate crop health range (0.33-0.66) on the NDVI scale.

Conclusions:

The study presents a novel mathematical approach linking soil moisture and quality to crop health quantification.
The model offers a practical framework for evidence-based agricultural decision-making, particularly for small-scale farmers.
Real-time data implementation is recommended for the proposed mathematical modeling.