Hybrid Models and Technological Configurations for Forecasting Soil-Meteorological Variables: An Exhaustive Review
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Introduction: Homemade designs of climate and soil monitoring stations have shown significant advances in recording, processing, calibration, and forecasting applications, particularly in sustainable agriculture. Technological development has promoted the use of low-cost air and soil sensors, enabling the creation of useful databases to improve measurements and spatio-temporal predictions.
Objective: To provide an exhaustive analysis of technological configurations, applications, trends, quality, and research needs in soil–meteorological monitoring and forecasting.
Materials and Methods: A review of published scientific articles was conducted, complemented with statistical analyses and data mining techniques, particularly hierarchical clustering, to identify patterns and relevant approaches in the field.
Results: Studies indicate that the calibration of air and soil sensors through mathematical, statistical, and artificial intelligence methods has enabled the generation of reliable records. These data have been used to predict weather and soil conditions, as well as future levels of nutrients and contaminants. Moreover, there is a growing trend toward the use of hybrid models that combine two or more forecasting methods, together with meteorological and edaphic stations equipped with diverse sensors.
Conclusions: The integration of low-cost technologies and hybrid models strengthens the accuracy of soil–meteorological records and forecasts. However, challenges remain in improving data quality and consolidating research trends to guide future advancements in the field.
- Artificial intelligence
- Data mining
- monitoring station
- Soil forecasting
- Weather forecasting
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