Peer review

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.

Text in English

Fragility to Resilience in Central and West Asia and North Africa Indian Council of Agricultural Research (ICAR)