Estimation of Wild Blueberry Fruit Yield Using Digital Color Photography

Abstract

The wild blueberry industry of North America may benefit significantly from precision agriculture technology. Currently, crop management practices are implemented on an average basis without considering the substantial variation in soil properties, bare spots, topographic features, and fruit yield in blueberry fields. Yield maps along with fertility, weed, and topographic maps can be used to generate prescription maps for site-specific application of agrochemicals (e.g., fertilizer or pesticide). Two wild blueberry fields in central Nova Scotia were selected to evaluate a photographic method for fruit yield estimation. A 10-megapixel 24-bit digital color camera was mounted on a tripod and pointed downwards to take photographs of the blueberry crop from a height of approximately 1 m. At harvest time, blueberry crop images were collected in each field at 30 different sample locations displaying a range in yield. Actual fruit yield was sampled from the same locations by hand-harvesting out of a 0.5 0.5 m quadrat using a commercial blueberry rake. Custom image processing software was developed to count the blue pixels of ripe fruit in the quadrat region of each image and express it as a percentage of total quadrat pixels. Linear regression was used to calibrate the fruit yield with percentage blue pixels separately in each field, and then the calibration equation of field 1 was used to predict fruit yield in field 2 for validation of the method. Percentage blue pixels correlated highly significantly with hand-harvested fruit yield in field 1 (R2 = 0.98; P < 0.001; n = 30) and field 2 (R2 = 0.99; P < 0.001; n = 30). The correlation between actual and predicted fruit yield in field 2 (validation) was also highly significant (R2 = 0.99; P < 0.001; n = 30; RMSE = 277 kg/ha). Non-significance of the t-test for actual versus predicted yield indicated that there was no significant bias in the yield estimation and that the predicted yield was accurate. Based on these results, an automated yield monitoring system consisting of a digital camera, computer, and DGPS will be developed and incorporated into a harvester to monitor and map blueberry fruit yield in real time.