Non-invasive sensing techniques to phenotype multiple apple tree architectures

• Published in: Information processing in agriculture Vol. 10; no. 1; pp. 136 - 147
• Main Authors: Zhang, Chongyuan, Serra, Sara, Quirós-Vargas, Juan, Sangjan, Worasit, Musacchi, Stefano, Sankaran, Sindhuja
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2023; Elsevier
• Subjects: Image analysis; Tree row volume; Tree training systems; Unmanned aerial vehicle; Image analysis; Tree training systems; Unmanned aerial vehicle; Tree row volume
• ISSN: 2214-3173; 2214-3173
• DOI: 10.1016/j.inpa.2021.02.001

•Apple architectural tree traits grown under different conditions were evaluated using sensors.•Significant correlations between box-counting dimension with tree height and yield were found.•Tree row volume was correlated with ground reference data (trunk area, total fruit yield/tree). Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years. Further, the tree fruit architecture contributes to the light interception and improves tree growth, fruit quality, and fruit yield, in addition to easing the process of orchard management and harvest. Currently tree architectural traits are measured manually by researchers or growers, which is labor-intensive and time-consuming. In this study, the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders, physiologists and growers in collecting architectural traits efficiently and in a standardized manner. For this, a consumer-grade red–green–blue (RGB) camera was used to collect apple tree side-images, while an unmanned aerial vehicle (UAV) integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures. The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA 38 trees grafted on G41 and M9-Nic29) and two pruning methods (referred as bending and click pruning). The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model. The traits extracted from sensing data included box-counting fractal dimension (DBs), middle branch angle, number of branches, trunk basal diameter, and tree row volume (TRV). The results from ground-based sensing data indicated that there was a significant (P < 0.0001) difference in DBs between Spindle and V-trellis training systems, and correlations between DBs with tree height (r = 0.79) and total fruit yield per unit area (r = 0.74) were significant (P < 0.05). Moreover, correlations between average or total TRV and ground reference data, such as tree height and total fruit yield per unit area, were significant (P < 0.05). With the reported findings, this study demonstrated the potential of sensing techniques for phenotyping tree fruit architectural traits.