DESIGN AND EXPERIMENT OF GRAIN HARVESTER YIELD MONITORING SYSTEM BASED ON MULTI-SENSOR FUSION

Precision agriculture requires accurate and efficient crop yield distribution information. However, both traditional field-based yield measurement methods and existing combine harvester yield monitoring systems face significant limitations. Traditional methods, such as direct weighing or sampling, are time-consuming and inefficient, and they only provide average yield values - insufficient for large-scale farming needs. Meanwhile, current monitoring systems often suffer from high measurement errors, low spatial resolution, and limited generalizability. For this reason, this study designs a new type of grain yield monitoring system, which corrects the photoelectric sensor data through the load cell data, realizes the calibration of the photoelectric sensor, avoids the influence of external factors, and improves the accuracy of measurement. Firstly, tests were carried out at three rotational speeds of 10 Hz, 20 Hz and 25 Hz of the motor inverter setting, respectively, to determine the positive proportionality coefficient between the photoelectric signal and the grain mass, and the overall error of the system was measured to be less than 6.44%. For the load cell, a model of the relationship between tilt angle and weighing accuracy was established and a compensation algorithm was proposed, the weighing error data in different directions and at different tilt angles were measured and analyzed, and a mathematical model between the corrected angle and the weighing error was established. Through the tilting experiment, the feasibility of the modified angle compensation model is verified, and the overall error after compensation is less than 0.25%, and the systematic error of measurement and production after the intervention of the feedback system is less than 0.74%. The experimental results demonstrate that the system significantly enhances the accuracy and stability of yield measurement. It holds substantial potential for widespread application, provides strong support for the advancement of precision agriculture, and is expected to drive agricultural production toward greater efficiency and sustainability.