Barley Yield Estimation Using Regression Models and Spatial Pattern Analysis

Precision agriculture (PA) has emerged as a key strategy for optimizing agricultural production. Using data-driven technologies such as sensors and satellite imagery, PA improves the efficiency of agricultural processes. Accurate crop yield estimation is an essential component of PA. An important aspect of yield estimation within PA is the ability to assess and map spatial variations in yield in an agricultural field. Understanding these spatial patterns enables more precise management decisions and targeted interventions.

Therefore, this study aimed to develop two regression approaches, multiple linear regression (MLR) and random forest regression (RFR), to estimate crop yield using sixteen input variables with a 6 m resolution. These variables were obtained using different sensors, reflecting the soil and crop spatial variability. The estimation performance of the studied approaches was assessed using the coefficient of determination (R²), showing very satisfactory results (R² > 0.85) for both approaches.

The spatial distribution of barley yield was assessed, focusing on identifying areas of high and low productivity within the field. RFR demonstrated its ability to capture yield patterns. By incorporating spatial factors, RFR effectively modelled the varying yield potential in the crop field.

 

Keywords—precision agriculture, multiple linear regression, random forest regression, spatial pattern, barley

 

Acknowledgments: Financed by Ministerio de Ciencia e Innovación, Spain (PID2021-124041OB-C22)