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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农业人工智能 土壤墒情
SMART IRRIGATION STRATEGY RECOMMENDED SYSTEM USING ML
提出基于ESP32传感与两阶段机器学习的智能灌溉推荐系统，预测土壤湿度并推荐灌溉动作。
Automated System for Plant Health Monitoring and Stress Diagnosis Using IoT and Machine Learning
设计并验证了基于ESP32与随机森林的物联网闭环灌溉系统，实现植物胁迫自动诊断。
A new vegetation–thermal integrated approach (NNL-SM) for high-resolution soil moisture estimation
提出NNL-SM模型，融合NDMI、NDVI和LST将SMAP土壤水分从9km降尺度至1km。
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。
Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture
该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。