US 11,915,366 B2
Per-plant aerial image data analytics method and devices
Alireza Pourreza, Sacramento, CA (US)
Assigned to The Regents of the University of California, Oakland, CA (US)
Filed by The Regents of the University of California, Oakland, CA (US)
Filed on Apr. 27, 2021, as Appl. No. 17/241,531.
Prior Publication US 2022/0343593 A1, Oct. 27, 2022
Int. Cl. G06T 17/00 (2006.01); G06T 15/80 (2011.01); G01S 17/89 (2020.01); G06V 10/26 (2022.01); G06V 10/75 (2022.01); G06V 20/10 (2022.01)
CPC G06T 17/00 (2013.01) [G01S 17/89 (2013.01); G06T 15/80 (2013.01); G06V 10/26 (2022.01); G06V 10/751 (2022.01); G06V 20/188 (2022.01); G06V 2201/121 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
creating 3D models of an orchard with multiple plants in the form of a densified point cloud using oblique aerial RGB imaging and photogrammetry;
identifying and segmenting individual plants of the orchard from the 3D models;
simulating sunlight radiation of each plant in the 3D models;
determining a shading effect of branches and neighboring plants on each individual plant at any time of the day;
determining canopy light interception of each plant;
extracting and analyzing canopy profile features of each plant in the 3D models;
forecasting potential yield of each plant based on the measured canopy light interception;
calculating nitrogen and water requirements of each plant based on the potential yield and other predetermined field, environmental and climate factors; and
providing a measured actual yield for each plant to validate yield forecasting by canopy light interception data.