Enhancing soil organic carbon estimation with generative AI and Nix color sensor.

Area of Science:

• Soil Science
• Agricultural Technology
• Data Science

Background:

• Soil organic carbon (SOC) is crucial for soil health but difficult to measure conventionally.
• Conventional soil assays are labor-intensive and costly, hindering widespread soil health monitoring.

Purpose of the Study:

• To investigate a rapid, low-cost alternative for SOC prediction using a handheld color sensor and data-driven models.
• To enhance prediction accuracy by employing synthetic data augmentation techniques, particularly for underrepresented SOC ranges.

Main Methods:

• Utilized a Nix Spectro 2 Color Sensor to capture soil color data from 641 samples.
• Employed Random Forest (RF), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) prediction engines.
• Applied generative artificial intelligence (AI) techniques (GANs, GMM) and statistical methods (KNN, bootstrapping) for synthetic data augmentation.

Main Results:

• The baseline Random Forest model achieved R² = 0.71.
• Augmenting the calibration set with 44 GMM-generated samples improved RF performance to R² = 0.77, reducing bias and improving SOC distribution coverage.
• Synthetic data integration enhanced model generalization and predictive accuracy, mitigating issues from variance differences between datasets.

Conclusions:

• Integrating a Nix color sensor with AI-driven synthetic data augmentation offers a rapid, cost-effective solution for on-site soil organic carbon assessment.
• This approach shows significant potential for precision agriculture and sustainable soil management.
• Future work should extend these AI-driven methods to multi-parameter soil analysis and digital soil mapping.