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Non-invasive sensing techniques to phenotype multiple apple tree architectures

Information Processing in Agriculture 2023年第1期10卷 136-147页

作者： Chongyuan Zhang Sara Serra Juan Quiro´s-Vargas worasit sangjan stefano musacchi Sindhuja Sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWA 99164USA Tree Fruit Research&Extension Center Washington State UniversityWenatcheeWA 98801USA Department of Horticulture Washington State UniversityPullmanWA 99164USA

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development ***,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 *** tree architectural traits are measured manually by researchers or growers,which is labor-intensive and *** 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 *** 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 *** sensing data were compared to ground reference data associated with tree orchard blocks within three training systems(Spindle,V-trellis,Biaxis),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 *** 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 r

关键词： Tree training systems Tree row volume Unmanned aerial vehicle Image analysis

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