TY  - JA
AU  - Roy,D.
AU  - Ghosh,T.
AU  - Das,B.
AU  - Jatav,R.
AU  - Chakraborty,D.
TI  - Smartphone-based image analysis and interpretable machine learning for soil moisture estimation across diverse Indian soils
SN  - 2352-9385
PY  - 2025///
CY  - Amsterdam (Netherlands)
PB  - Elsevier B.V., 
KW  - Machine learning
KW  - AGROVOC
KW  - Soil
KW  - Soil Water Content
KW  - Monitoring
KW  - Remote sensing
N1  - Peer review
N2  - Reliable soil moisture estimation is crucial for agricultural water management, yet conventional methods are often invasive, costly, and impractical for frequent field-level use. This study presence a smartphone-based, non-destructive approach for estimating soil moisture content (SMC) estimation across five contrasting Indian soil groups from 14 locations. A total of 238 soil images were analyzed to extract 33 colour-based features, which were then used to train and validate ten machine learning (ML) models. The Random Forest (RF) model exhibited the highest predictive accuracy (R2 = 0.78; RMSE = 5.98 %) during validation. To improve interpretability, SHAP and ALE techniques identified Redness Index (RI), Colour Feature Index (ColFeatInd), red band (R), value (V), and X colour space as key predictors. Boruta selection confirmed the relevance of all features. This study demonstrates the potential of combining smartphone imagery and interpretable ML to scalable, low-cost SMC across diverse soil types
DO  - https://doi.org/10.1016/j.rsase.2025.101655
T2  - Remote Sensing Applications Society and Environment
ER  -