Multi-output linear regression for real-time prediction of environmental sensor indicators in smart agriculture

A hybrid model for agricultural pollution load forecasting by integrating meteorological data and soil properties: an XGBoost-LSTM approach

A hybrid forecasting model that fuses eXtreme Gradient Boosting for spatial feature importance evaluation with Long Short-Term Memory (LSTM) networks for sequential load prediction is proposed that provides a robust tool for proactive nutrient runoff management in data-sparse agricultural contexts.

Smart Weather Station for Real-Time Climate Monitoring and Prediction Using IoT and Machine Learning Technologies

Accurate hyper-local climate prediction is essential for decision-making in agriculture, urban planning, and environmental management. This study addresses the scarcity of accessible monitoring solutions by developing an autonomous, IoT-enabled smart weather station that integrates high-frequency data acquisition with...

Feature Engineering and Crop Yield Prediction using Agricultural Time-Series Data

The increasing availability of agricultural timeseries data enabled more accurate and data-driven crop yield prediction. However, raw meteorological, soil, and vegetation datasets often fail to capture complex temporal dependencies essential for robust forecasting. This study proposes a structured feature engineering...

UPMO: An Uncertainty-Aware Predictive Multi-Objective Optimization Framework for Smart Agriculture

Smart agriculture and agro-industrial systems increasingly rely on sensor-driven data to optimize resource utilization while maintaining high productivity. However, these systems involve inherently conflicting ob j ectives, par ticularly minimizing energy consumption while maximizing yield efficiency. This paper presen...

Cloud-Assisted Smart Agriculture System using IoT Sensors And Python-Driven Machine Learning for Crop Yield Optimization and Soil Health Prediction

Noise-Augmented Boosting Framework for Accurate Soil Temperature Prediction

Soil temperature prediction is important for farming, climate research, and environmental modeling. This research proposes an ensemble prediction method for soil temperature prediction on a daily basis using lag feature and Gaussian noise. In the proposed framework, the ensemble algorithms Extreme Gradient Boosting (XG...