Smartphone-based image analysis and interpretable machine learning for soil moisture estimation across diverse Indian soils

IF 4.5 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-07-06 DOI:10.1016/j.rsase.2025.101655

Debasish Roy , Tridiv Ghosh , Bappa Das , Raghuveer Jatav , Debashis Chakraborty

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引用次数: 0

Abstract

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.

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基于智能手机的图像分析和可解释的机器学习，用于估算不同印度土壤的土壤湿度

可靠的土壤湿度估算对农业水资源管理至关重要，然而传统的方法往往是侵入性的，昂贵的，并且不适合频繁的田间使用。本研究提出了一种基于智能手机的非破坏性方法，用于估算来自14个地点的五个不同的印度土壤组的土壤水分含量（SMC）。总共分析了238张土壤图像，提取了33个基于颜色的特征，然后用于训练和验证10个机器学习（ML）模型。随机森林（Random Forest， RF）模型的预测精度最高(R2 = 0.78；RMSE = 5.98%)。为了提高可解释性，SHAP和ALE技术确定了红度指数（RI）、颜色特征指数（colfeind）、红带(R)、值(V)和X颜色空间作为关键预测因子。博鲁塔的选择证实了所有特征的相关性。这项研究展示了将智能手机图像和可解释的ML结合到不同土壤类型的可扩展、低成本SMC的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems