Abstract
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. Keywords: canopy vigor, UAV imagery, digital surface model, kiwifruit plant, missing plants, photogrammetry, plant stress DOI: 10.25165/j.ijabe.20191201.4634 Citation: Xue J R, Fan Y M, Su B F, Fuentes S. Assessment of canopy vigor ınformation from kiwifruit plants based on a digital surface model from unmanned aerial vehicle ımagery. Int J Agric & Biol Eng, 2019; 12(1): 165–171.
Highlights
Kiwifruit is an important cash value crop and their cultivation has become a major agricultural industry that promotes the economic development of the burgeoning kiwifruit producing regions in the northwest of China, the northeast of New Zealand, as well as Italy
Plant biotic and abiotic stresses can result in a partial or complete canopy and plant losses within an orchard. These effects can lead to significant economic losses depending on the severity of the specific detrimental effects which have a direct relationship with the production and quality of kiwifruits in the short and long term
The Digital Surface Model (DSM) allows only qualitative analysis of canopies height in relation to ground, the area corresponding to missing plant cannot be assessed directly via the DSM of study region because of the complicated surface structures of the undulating ground in the kiwifruit orchard
Summary
Kiwifruit is an important cash value crop and their cultivation has become a major agricultural industry that promotes the economic development of the burgeoning kiwifruit producing regions in the northwest of China, the northeast of New Zealand, as well as Italy. Plant biotic and abiotic stresses can result in a partial or complete canopy and plant losses within an orchard. These effects can lead to significant economic losses depending on the severity of the specific detrimental effects which have a direct relationship with the production and quality of kiwifruits in the short and long term. It is urgent to develop fast, cost-effective and near real-time methods to obtain more accurate information from kiwifruit orchards that reflect accurately the spatial and temporal variability at the plant level for efficient decision making and crop management to maximize fruit quality and yield, especially in a changing climate. Most of the visible/near-infrared/thermal infrared indices that can be calculated from UAS remote sensing do not require atmospheric corrections, compared to satellite remote sensing[8] due to low altitude surveys required (50-100 MAGL)
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