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Related Experiment Video

Updated: Feb 19, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Image-based machine learning models for customized soil moisture management.

Yooan Kim¹, Taehyeong Kim^2,3, Sungyong Lee⁴

¹Institute of Construction and Environmental Engineering, Seoul National University, Seoul, South Korea.

|February 17, 2026

Summary

Smart farming can be improved with AI. This system uses sensors and images to monitor individual plant soil moisture, optimizing crop management and yields.

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Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

Area of Science:

Agricultural Engineering
Artificial Intelligence in Agriculture
Precision Agriculture

Background:

Conventional smart farming applies uniform treatments, failing to address individual crop needs due to microenvironmental variations.
Inefficient resource use and reduced yields result from overlooking plant-specific requirements.
Non-invasive methods for individual plant soil monitoring are lacking, hindering customized crop management.

Purpose of the Study:

To develop an AI-based system for customized soil moisture management.
To enable non-invasive monitoring of soil conditions at the individual plant level.
To improve smart farming efficiency and crop yields through plant-specific data.

Main Methods:

Utilized RGB images and soil sensor data (3cm, 10cm, 15cm depths) for wild-simulated ginseng.
Employed deep learning models (DenseNet121) for surface moisture prediction.
Applied random forest regression for deeper soil layer moisture dynamics.

Main Results:

DenseNet121 achieved high accuracy (R²=97.3%) for surface moisture prediction.
Random forest regression demonstrated strong performance (R²=90.6%) for deeper soil layers.
Validated the non-invasive estimation of soil moisture using surface image data.

Conclusions:

AI-driven image and sensor analysis enables customized soil moisture management.
The system supports data-driven, plant-specific approaches in smart farming.
Future work should involve diverse crop/soil validation and spectral data integration.