IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 455 - 480

Опубликована: Апрель 11, 2025

Soil organic carbon (SOC) is an essential element of the global cycle, serving a central role in climate change mitigation, soil fertility, and ecosystem sustainability. Conventional SOC estimation techniques are time-consuming, labor-intensive, geographically confined, thus confining their efficiency for large-scale monitoring. This chapter discusses how artificial neural networks, such as CNNs, RNNs, deep learning models, improve forecasting accuracy scalability. With integration remote sensing, geospatial data, environmental factors, AI-based models facilitate effective processing mapping distribution. Deep machine methodologies enhance predictive power, automate analysis, mitigate uncertainties estimation. Critical methodologies, issues, emerging trends exploiting networks storage discussed, prioritizing sequestration monitoring optimization, sustainable land management, resilience planning.

Язык: Английский