Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery

Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development. By implementing a Digital Surface Model (DSM) to imagery obtained using Unmanned Aerial Vehicles (UAV), it is possible to filter canopy information effectively based on height, which provides an efficient method to discriminate canopy from soil and lower vegetation such as weeds or cover crops. This paper describes a method based on the DSM to assess canopy growth (CG) as well as missing plants from a kiwifruit orchard on a plant-by-plant scale. The DSM was initially extracted from the overlapping RGB aerial imagery acquired over the kiwifruit orchard using the Structure from Motion (SfM) algorithm. An adaptive threshold algorithm was implemented using the height difference between soil/lower plants and kiwifruit canopies to identify plants and extract canopy information on a non-regular surface. Furthermore, a customized algorithm was developed to discriminate single kiwifruit plants automatically, which allowed the estimation of individual canopy cover fractions (fc). By applying differential fc thresholding, four categories of the CG were determined automatically: (i) missing plants; (ii) low vigor; (iii) moderate vigor; and (iv) vigorous. Results were validated by a detailed visual inspection on the ground, which rendered an overall accuracy of 89.5% for the method proposed to assess CG at the plant-by-plant level. Specifically, the accuracies for CG category (i)- (iv) were 94.1%, 85.1%, 86.7%, and 88.0%, respectively. The proposed method showed also to be appropriate to filter out weeds and other smaller non-plant materials which are extremely difficult to be distinguished by common colour thresholding or edge identification methods.