Yield Monitoring and Mapping in Precision Agriculture
Crop Yield Monitoring
Crop yield monitoring is the process of measuring crop production in the field during harvest, often using sensors on combines or other harvesting equipment. This data, combined with GPS location information, allows for the creation of yield maps, which can be used to analyze crop performance and identify areas for improvement.
Crop Yield Monitoring Techniques
Remote Sensing-Based Yield Estimation
Remote sensing–based yield estimation involves using satellite, UAV, or aerial imagery to predict crop yields by analyzing vegetation characteristics over time. It provides spatially comprehensive, non-destructive, and timely yield insights, supporting precision agriculture, resource planning, and food security forecasting. Vegetation indices (VIs) have been widely used to predict crop yields over the past few decades.
Crop Modeling, Weather, and Soil Data
Integrating crop models with weather and soil data enables process-based yield estimation. Unlike purely statistical or remote sensing approaches, crop models simulate the physiological growth of crops based on environmental conditions and management practices. This provides a powerful tool for predictive analytics, decision support, and scenario testing in precision agriculture.
Ground Sampling and Yield Estimation
Ground sampling and crop yield estimation is a fundamental approach in precision agriculture used to assess and estimate crop productivity through direct field measurements. It complements remote sensing and model-based techniques, offering high-accuracy, ground-truth data. Ground sampling involves collecting physical samples of crop biomass or harvested yield from specific, georeferenced locations within a field. These samples are then analyzed to estimate actual or potential yield.
Machine Learning / AI-Enhanced Techniques
Machine learning (ML) and artificial intelligence (AI) techniques are transforming how crop yield is estimated by integrating multi-source data—including remote sensing, weather, soil, and management data—to produce accurate, scalable, and real-time yield predictions. AI-enhanced yield estimation involves training models using large datasets to recognize complex patterns and relationships between crop growth indicators and final yield outcomes. These models can predict yield before harvest, monitor spatiotemporal trends, and support precision agriculture decisions.
Combine Harvester-Based Crop Yield Monitoring
Combine harvester-based yield monitoring is a core technology in precision agriculture, enabling real-time, geo-spatially-referenced crop yield data per acre during harvest. The integration of sensors on the combine allows for the automatic recording of yield, protein content, moisture content of grain, and location, producing detailed yield maps for farm management and decision-making. Various combinations of these data permit the computation of secondary crop attributes such as grain nitrogen removal and harvest index, which are useful post-harvest bio-indicators of environmental stress and when and where it occurred in the growing season. When combined with positioning systems such as DGPS, site-specific yield maps can be generated.
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