Non-invasive sensing techniques to phenotype multiple apple tree architectures
| Autor: | Worasit Sangjan, Sindhuja Sankaran, Juan Quirós-Vargas, Stefano Musacchi, Chongyuan Zhang, Sara Serra |
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| Rok vydání: | 2023 |
| Předmět: |
Tree canopy
business.industry Computer science 020209 energy 010401 analytical chemistry Apple tree Forestry Pattern recognition 02 engineering and technology Aquatic Science 01 natural sciences Trunk 0104 chemical sciences Computer Science Applications Tree (data structure) 0202 electrical engineering electronic engineering information engineering RGB color model Animal Science and Zoology Artificial intelligence Orchard Interception business Agronomy and Crop Science Pruning |
| Zdroj: | Information Processing in Agriculture. 10:136-147 |
| ISSN: | 2214-3173 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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