Explainable Machine Learning Framework for Soil Moisture Stress Assessment and Irrigation Decision Support
构建可解释机器学习框架，预测次日土壤湿度并支持灌溉决策。
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#土壤湿度预测
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没有这个标签，内容接近Three-way debate – measurements, pedotransfer functions, and inverse modeling – input strategy for soil hydraulic parameters in wheat simulations
比较测量、传递函数和反演建模获取土壤水力参数对小麦模拟的影响。
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Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data
用多模态遥感与机器学习预测印度锡金高降雨区表层土壤水分。
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From sensing to modeling: machine learning-driven wearable humidity sensors for plant water status monitoring
将可穿戴湿度传感器与机器学习结合，实现植物水分状态监测。
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Development of Internet of Things-Based Soil Sensors for pH and Phosphorus Monitoring in Paddy Fields: Development, Accuracy, and Implementation Challenges
综述2019-2026年稻田pH与磷的物联网土壤传感器发展、精度及实施挑战。
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Accurate diagnosis of soil moisture and canopy water content in citrus orchards based on synergistic multi-source remote sensing
融合多源遥感数据改进水云模型，实现柑橘园土壤水分和冠层含水量高精度反演。
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Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province
基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。
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SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION
基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。
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