Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Abstract

Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. Individual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies >96%. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

Current Practices in UAS-based Environmental Monitoring

“…As GCPs still provide the most accurate results, a step in mitigating their time-consuming placement and geolocation are using specially designed GCPs with integrated GNSS, such as Propeller ® AeroPoints [175]. The location of the AeroPoints can be recorded for a couple of hours, automatically uploaded after the flight and subsequently post-processed [95,176]. The type of GCPs deployed also depends on the sensor choice, as for low spatial resolution and low-contrast thermal sensors, aluminum GCPs are needed, reflecting up to 90% of thermal radiation [177].…”

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…The images are used to retrieve dimensional properties of the crop, terrain configuration, macrostructure of the field, and the spatial information. Based on the dimensional properties, such as size, height, perimeter, and area of the crown, the resource need practices can be estimated [119][120][121]. Generally, a larger crop is expected to more quickly use available water resources, resulting in crop water stress at a later stage of the season if irrigation is not sufficient.…”

“…Using the SfM on remotely-sensed images, 3D canopy structure, terrain configurations, and canopy surface models can be derived [113,114,119,186,232]. By employing a delineation algorithm on the 3D models, the 3D attributes of the crops and macrostructure are determined more accurately [120,122,233]. Crop surface area and terrain configuration (e.g., slope and aspect) may help to develop an optimal resource management strategy.…”

Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

“…Above referred methods and other image segmentation methods in this special issue were mainly used for extracting the target morphological traits, while reducing the interference from the background information. Other morphological traits such as crown height, extent, volume, and diameter were mainly derived and processed from three-dimensional point clouds by structure from motion algorithms [8,19,30,31,33]. artificial neural network (ANN), etc., were usually preferred and recommended [10,[14][15][16][17][18]21,[23][24]32].…”

“…These structural traits were determined by three-dimensional point clouds from RGB imagery or LiDAR scans. Tu et al [31] and Patrick et al [8] established a structure from motion algorithm (SfM) for accurately estimating crown height, extent, plant projective cover in avocado tree and Blueberry bush, respectively. Wang et al [30] compared three representative 3D data acquisition approaches, including 3D Laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates, and these approaches with respect to leaf length, width, inclination angle, azimuth, area, and height in Maize.…”