Non-invasive sensing techniques to phenotype multiple apple tree architectures

Zhang, Chongyuan; Serra, Sara; Quiros-Vargas, Juan; Sangjan, Worasit; Musacchi, Stefano; Sankaran, Sindhuja

doi:10.1016/j.inpa.2021.02.001

Cited by 10 publications

(9 citation statements)

References 42 publications

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“…In addition to the traits associated with canopy height, crown volume, crown area, and crown diameter, other traits can be evaluated, such as light interception (Sampson and Smith, 1993;Willaume et al, 2004;Buler and Mika, 2009;Tang et al, 2019;de Mattos et al, 2020;Anthony et al, 2020) and fractal dimension (Zhang et al, 2011(Zhang et al, , 2021Guzmán et al, 2020), using sensing techniques as an indirect measurement of the canopy architecture. Many studies have reported the application of sensing systems for measuring light interception using sensing and analysis methods similar to those described above.…”

Section: Other Traitsmentioning

confidence: 99%

“…Differences in fractal dimension numbers were found based on the tree age. Similarly, Zhang et al (2021) evaluated the box-counting fractal dimension estimated from RGB images (2D data) of apple trees with three training systems and found a strong correlation (r > 0.74, p < 0.05) with tree height and total fruit yield per unit area. LiDAR data (TLS and ALS) can also be used for estimating the fractal dimension.…”

Section: Other Traitsmentioning

confidence: 99%

“…In both agriculture and forestry, the similarity of the canopy may present challenges with point, feature, or object matching during the stitching (orthomosaic generation) process. Imaging with good contrast (e.g., tree fruit or tree with snow background in the absence of leaf cover to increase the contrast between soil and branch) and using ground control points can be useful in such cases (Zhang et al, 2021). Optimum environmental conditions (e.g., low wind, solar angle, solar intensity, etc.)…”

Section: Advancements and Practical Considerationsmentioning

confidence: 99%

“…Overall, tree architecture can affect the performance of different tree varieties with respect to their training system. The tree architecture affects growth and development, light interception and photosynthesis, and crop yield and fruit quality (Zhang et al, 2021). Given the development of new training systems, tree architecture (growth pattern and Submitted for review on 30 November 2020 as manuscript number MS 14419; approved for publication as an Invited Review Article and as part of the UAVs in Agriculture Collection by the Machinery Systems Community of ASABE on 2 August 2021.…”

mentioning

confidence: 99%

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Phenotyping Architecture Traits of Tree Species Using Remote Sensing Techniques

Sangjan¹,

Sankaran²

2021

Transactions of the ASABE

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HighlightsTree canopy architecture traits are associated with its productivity and management.Understanding these traits is important for both precision agriculture and phenomics applications.Remote sensing platforms (satellite, UAV, etc.) and multiple approaches (SfM, LiDAR) have been used to assess these traits.3D reconstruction of tree canopies allows the measurement of tree height, crown area, and canopy volume.Abstract. Tree canopy architecture is associated with light use efficiency and thus productivity. Given the modern training systems in orchard tree fruit systems, modification of tree architecture is becoming important for easier management of crops (e.g., pruning, thinning, chemical application, harvesting, etc.) while maintaining fruit quality and quantity. Similarly, in forest environments, architecture can influence the competitiveness and balance between tree species in the ecosystem. This article reviews the literature related to sensing approaches used for assessing architecture traits and the factors that influence such evaluation processes. Digital imagery integrated with structure from motion analysis and both terrestrial and aerial light detection and ranging (LiDAR) systems have been commonly used. In addition, satellite imagery and other techniques have been explored. Some of the major findings and some critical considerations for such measurement methods are summarized here. Keywords: Canopy volume, LiDAR system, Structure from motion, Tree height, UAV.

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Section: Other Traitsmentioning

confidence: 99%

Section: Other Traitsmentioning

confidence: 99%

Section: Advancements and Practical Considerationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Phenotyping Architecture Traits of Tree Species Using Remote Sensing Techniques

Sangjan¹,

Sankaran²

2021

Transactions of the ASABE

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“…Overall, the aerial flight mission with a double grid mission and sensor inclination angle of 65 • provided accurate measurements and had greater significance in the development of site-specific higher quality and precise canopy vigor maps [24]. A ground-based 2D and UAV-based 3D model was also used to evaluate the critical architectural traits of apple trees [25]. The extracted traits, such as D B S (box-counting fractal dimension), middle branch angle, number of branches, trunk basal diameter, and tree volume, were compared with the ground reference datasets associated with the three training systems (spindle, V-trellis, and bi-axis), two rootstocks, and two pruning methods in the apple trees.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees

Raman

Carlos

Sankaran

2022

Sensors

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Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University’s Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.

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