AI technologies, such as machine learning and computer vision, enable precise, real-time monitoring of soil health in regenerative agriculture by analyzing remote sensing and spectral data to estimate soil organic matter (SOM). This analysis utilizes metagenomic data and sensor inputs to model microbial activity, and predictive algorithms are applied to assess nutrient dynamics (N, P, K). These tools provide scalable, non-invasive, and data-driven insights for optimizing soil management, enhancing carbon sequestration, and improving biological soil functions, thereby supporting sustainable and resilient agroecosystems.

Significantly improve soil health monitoring in regenerative agriculture systems. Machine learning algorithms can analyze data from a variety of sources, such as soil sensors, satellite imagery, and weather patterns, to assess soil organic matter, microbial activity, and nutrient dynamics. For example, computer vision can be used to analyze images of soil samples and identify properties related to their texture and composition. AI-powered tools can also predict how different management practices affect soil health over time, allowing farmers to make informed decisions. Additionally, real-time monitoring via IoT devices can provide continuous feedback on soil conditions, enabling timely interventions. By integrating these technologies, farmers can improve soil health, enhance biodiversity, and enhance crop yields, ultimately supporting sustainable agricultural practices.

AI technologies—particularly machine learning, deep learning, and computer vision—are proving highly effective in monitoring and enhancing soil health within regenerative agriculture systems. These tools can analyze multi-source data (e.g., satellite imagery, in-field sensors, drone footage) to assess soil organic matter trends, microbial indicators, and nutrient dynamics. AI models can also predict degradation risks and recommend site-specific interventions like cover cropping or compost applications.

In my paper, “Advanced Crop Recommendation System”, I developed a deep learning–fuzzy logic hybrid framework that factors in soil and climate data to optimize crop choices. This approach can be extended to regenerative systems by incorporating microbial, carbon, and moisture metrics, enabling proactive soil stewardship through AI.

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Article ADVANCED CROP RECOMMENDATION SYSTEM: LEVERAGING DEEP LEARNIN...