Phenotyping Architecture Traits of Tree Species Using Remote Sensing Techniques

Abstract

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.

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

“…In recent years, researchers have utilized sensor-based technology coupled with UAV systems in various applications for monitoring and decision-making in orchard management [29]. UAV sensing techniques have been used to extract the architectural traits in tree crops, such as canopy height, volume, and other structures [30,31]. This study focused on the importance and need for optimizing flight and sensor parameters in the extraction of architectural traits such as tree height and canopy crown volume.…”

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

“…Technological developments have allowed researchers to process massive data and obtain measurements of large portions of land. Unmanned aerial vehicles have been used in recent years as mechanisms to gather massive information about various ecosystems (Eugenio et al, 2020;Osco et al, 2021;Sangjan and Sankaran, 2021). Coupling UAVs with computer vision systems (RGB, multi-spectral, hyper-spectral and thermal cameras) and other sensors as LiDAR has allowed researchers to estimate forest parameters like height, canopy cover, DBH, vegetation indexes (Abdollahnejad and Panagiotidis, 2020;Kopackova-Strnadovaé t al., 2021;Raddi et al, 2021;Malabad et al, 2022;Zhuo et al, 2022).…”

“…Machine learning (ML) models have been used as both classifiers and predictors. Forest structure parameters and tree phenotypic features are predicted using machine learning techniques with input data gathered from LiDAR, RGB, and Multi-spectral cameras (Shin et al, 2018;McClelland et al, 2019;Puliti et al, 2019;Abdollahnejad and Panagiotidis, 2020;Fan et al, 2020;Imangholiloo et al, 2020;Ahmad et al, 2021;Cai et al, 2021;Neuville et al, 2021;Sangjan and Sankaran, 2021;Yu et al, 2021). Predictions of leaf moisture, chlorophyll, and nitrogen content, have been achieved using machine learning methods (Watt et al, 2020;Lou et al, 2021;Raddi et al, 2021;Raj et al, 2021;Narmilan et al, 2022;Zhuo et al, 2022).…”