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

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

摘要

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。

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

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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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