Back to Contents4.2 Yield Monitoring in Grain Crops
Development of yield monitoring capabilities continues to evolve. Current research on yield monitoring has followed two branches of investigation. In the first, numerous efforts have been undertaken to evaluate and develop mass and volumetric grain flow sensing strategies at harvest on the combine. It is widely acknowledged that the mass flow of material through a combine affects the resulting yield determination, and is, in part, responsible for a portion of the variability in yield maps. This gives rise to the second focus area. Specifically, many studies have been conducted to develop reconstruction and filtering techniques to reduce the effect of combine dynamics and calibration inconsistencies in yield maps. A brief description of pertinent studies is provided as a basis and justification for the work proposed in this project.
De Baerdemaeker et al. (1985) reported on the development of an impulse type grain sensor, that is the basis of the majority of yield monitoring devices in use today. The impulse flow-measuring device was constructed using a 90-degree, long radius elbow with a load cell located at the outside periphery and in the middle of the bend. The tube was fed with flowing grain from above such that by accelerating the grain from a vertical to horizontal direction would create a resultant force. This momentum change was recorded as impulses using the load cell. Under laboratory test conditions the authors noted measurement errors that varied with grain moisture content, grain flow rate, and inclination of the elbow-load cell instrument. Vasichen and De Baerdemaker (1991) expanded the initial work by exchanging the original tube configuration for a curved plate. The authors proposed and evaluated an indirect calibration approach that used a linear relationship to correct the mass flow sensor data. A low-pass filter was used to condition the sensor signal. A worst case estimate of yield error for this modified device was set at 6%.Following De Baerdemaeker et al. (1985), Wagner and Schrock (1989) reported on the development of a pivoted auger sensor for determination of grain mass flow rates on a combine. Comparisons of measured versus actual plot yields (total mass) revealed error rates of less than three percent. Colvin (1990) reported on the development of a weigh bin system for combines, but no indication of system accuracy was given. Stafford et al. (1991) detailed the performance of a capacitance-based sensing technology for grain flow rates at the discharge bin-loading auger. Furthermore, these authors reported on the performance of a nucleonic device. Of the two devices it was noted that the capacitance-based sensing device required more frequent calibrations. Nevertheless, the accuracy of either device was found to be acceptable. Strubbe et al. (1996) reported the development of an optical volumetric flow-rate-sensing device. With this device the maximum deviation from a simple regression line was determined to be nine percent. This is an improvement over a single sensor array where the maximum deviation was 13 percent.
Studies comparing yield-monitoring methodologies have been somewhat limited. However, Auernhammer et al. (1993) reported on the evaluation of two yield-measurement devices over a two-year period. Each year evaluation occurred during the harvest of 300 ha of small grains. The first yield measurement system was a volumetric device based on Bae et al. (1987). The second device used radiometric principles to evaluate mass flow rate. Eighty percent of the measurement error with the radiometric device appeared randomly distributed while 50% of the error associated with the volumetric device could be attributed to the operator and calibration. Measurement accuracy was determined to be nearly identical for either system.
Bae et al. (1987) were the first to merge the concepts of volumetric grain flow measurements with field during harvest for the specific purpose of logging data to generate yield maps for grain sorghum. Combine position was determined using a microwave system with fixed and mobile transponders. This study also reports a method for correcting yield data by smoothing the grain flow data, and by modeling and assessing combine dynamics. While "moderate" accuracy was achievable the authors did note that yields in low yielding areas of the field were overestimated by 25% resulting form the averaging effect of the combine dynamics. Additional details of this combine modeling work were reported in a subsequent publication (Searcy et al., 1989). A noteworthy highlight of either work is the quantification of the time constants for grain flow rates as the threshing/separating mechanism was loaded (unloaded) as the machine entered (exited) the standing crop. Grain flow rates were modeled as a first order system with time constants and transport delays.
Efforts to reconstruct yield data and filter or smooth erroneous yield data has been the subject of several recent investigations. Stout et al. (1993) explored averaging and modeling techniques for the generation of yield maps from yield monitor data. These researchers applied both first and second order models to describe grain flow in the combine. A more significant element of their work involved application of arithmetic and moving average techniques to filter the data. Actual corn yields were found to be best approximated using either fourth order or 10 second moving averages. Birrell et al. (1995) investigated several models to reconstruct instantaneous grain yields from yield monitor data. Simple time delay and first order system models were used to correct the data from either sensing device. Perez-Munoz and Colvin (1996) investigated the interaction of measured parameters including moisture, ground speed, elevator speed, and grain impact force on the accuracy of yield prediction under both laboratory and field conditions. The authors concluded that an impact-based yield sensing system is "a good tool to obtain yield estimates for fields as it was originally marketed."