An automated yield monitoring system II for commercial wild blueberry double-head harvester

Wild blueberry fruit yield maps could be used to generate prescription maps for site-specific application of fertilizer to improve crop productivity. An automated yield monitoring system II (AYMS II) consisting of two @mEye color cameras, real time kinematics-global positioning system, custom software, and a ruggedized laptop computer was developed and mounted on a Specialized Farm Motorized Vehicle (SFMV) for real-time fruit yield mapping. Custom software was developed in C++ programming language to acquire and process image in real-time from both cameras and store the percentage of blue pixels in ruggedized laptop computer. Two wild blueberry fields were selected in central Nova Scotia to evaluate the performance of the AYMS II for fruit yield mapping. Calibration was carried out at 20 randomly selected data points in each field. The ripe fruit was hand-harvested out of a 2.0x0.7m quadrat at each selected point and camera images were also taken from the same points to calculate the percentage of blue pixels (fraction of blue pixels in the image). Linear regression analysis was performed to calibrate the actual fruit yield with the percentage of blue pixels. Real-time yield mapping was carried out with AYMS II. The estimated yield along with geo-referenced coordinates was imported into ArcGIS 9.3 software for detailed mapping. The AYMS II, hardware and software performed well to estimate wild blueberry fruit yield. The linear regression results showed highly significant relationship between the percentage of blue pixels and actual fruit yield in field 1 and field 2. The correlation between actual and predicted fruit yield (validation, using the equation from field 2) in field 1 and field 2 (validation, using the equation from field 1) was also highly significant. Maps developed in ArcGIS 9.3 showed substantial variability in fruit yield in both fields. The bare spots were coincided with no or low yielding areas in the fields. The viable AYMS II will be incorporated into wild blueberry harvester. This information obtained from AYMS II could be used to implement site-specific management practices within the blueberry fields to optimize productivity while minimizing the environmental impact of farming operations.