A structured survey categorizing AI models for soil moisture estimation into statistical time-series, geostatistical, classical ML, deep learning, and probabilistic/Bayesian approaches.
Frontiers in Forests and Global Change7, 1353011 (2024)
1 Pith paper cite this work, alongside 17 external citations. Polarity classification is still indexing.
1
Pith paper citing it
17
external citations · external index
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
A Survey on Data-Driven Models for Soil Moisture Regression and Classification
A structured survey categorizing AI models for soil moisture estimation into statistical time-series, geostatistical, classical ML, deep learning, and probabilistic/Bayesian approaches.