Abstract
Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.