Phenotyping tree architecture using proximal and remote sensing techniques

Primary author: Chongyuan Zhang
Co-author(s): Juan José Quirós Vargas; Sara Serra; Stefano Musacch; Worasit Sangjan; Sindhuja Sankaran
Faculty sponsor: Dr. Sindhuja Sankaran

Primary college/unit: Agricultural, Human and Natural Resource Sciences
Campus: Pullman


Tree architecture optimizes the light interception and improves tree growth, fruit quality, and yield with the goal of simplify orchard management and harvest. However, currently tree architectural traits are measured manually by researchers or growers. In this study, both proximal and 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 easily and efficiently. A red-green-blue (RGB) camera was used to collect proximal side-images of apple tree, while an unmanned aerial system integrated with RGB camera was programmed to image tree canopy at 15 m above ground level. The data were processed to extract architectural features from 2D images (proximal) and 3D digital surface model (remote sensing). The sensing data were compared to ground reference data that have three training systems (Spindle, V-trellis, Bi-axis), two rootstocks (‘WA38’ trees grafted on G41 and M9-Nic29) and two pruning methods (Bending and Click). The results from proximal sensing indicated that there was a significant (P < 0.0001) difference in box-counting fractal dimension (DBs) between Spindle and V-trellis training systems, and correlations between DBs with tree height (r = 0.78) and total yield per unit area in Mton/hectare (r = 0.70) was significant (P < 0.05). Moreover, correlations between average or total tree row volume and ground reference data, such as trunk area, total fruit yield per tree, were significant (P < 0.05). This study demonstrated the potential of sensing or phenotyping techniques in detecting tree architectural traits.