Assessment of an automated digital method to estimate leaf area index ( LAI ) in cherry trees

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The midday stem water potential (Ψs) and stomatal conductance (gs) have been traditionally used to monitor the water status of cherry trees (Prunus avium L.). Due to the complexity of direct measurement, the use of infrared thermography has been proposed as an alternative. This study compares Ψs and gs against crop water stress indexes (CWSI) calculated from thermal infrared (TIR) data from high-resolution (HR) and low-resolution (LR) cameras for two cherry tree cultivars: ‘Regina’ and ‘Sweetheart’. For this purpose, a water stress–recovery cycle experiment was carried out at the post-harvest period in a commercial drip-irrigated cherry tree orchard under three irrigation treatments based on Ψs levels. The water status of trees was measured weekly using Ψs, gs, and compared to CWSIs, computed from both thermal cameras. Results showed that the accuracy in the estimation of CWSIs was not statistically significant when comparing both cameras for the representation of Ψs and gs in both cultivars. The performance of all evaluated physiological indicators presented similar trends for both cultivars, and the averaged differences between CWSI’s from both cameras were 11 ± 0.27%. However, these CWSI’s were not able to detect differences among irrigation treatments as compared to Ψs and gs.

Section: General Descriptionmentioning

confidence: 99%

Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance

Carrasco-Benavides

Antunez-Quilobrán

Baffico-Hernández

et al. 2020

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“…The proposed urban tree monitoring system that uses an integrated camera on moving vehicles can automatically provide information on trees’ growth and water status changes, which can serve as a powerful decision-making tool for city councils for tree management (i.e., supply water requirement at appropriate times and tree lopping for power lines encroachment and public safety management). The reliability of the system is based on the growth and canopy architecture parameters and algorithms used, which have been successfully implemented for other trees such as eucalyptus [ 22 ], and tree crops such as cherry trees [ 38 , 40 ], apple trees [ 37 ], and grapevines [ 33 , 48 , 49 , 50 ]. Tree water stress algorithms have been used to describe the water status of many trees and crops [ 32 , 51 , 52 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…This study has been based on the integration of previously developed technology from our research group for the automated analysis of visible and infrared thermal imagery for different crops such as eucalyptus trees [ 22 ], grapevines [ 32 , 33 , 34 , 35 ], kiwi plants [ 36 ], apple trees [ 37 ], cherry trees [ 38 , 39 , 40 ], and cocoa plants [ 41 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

Fuentes

Tongson

Viejo

2021

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Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.

“…Two upward-looking images (Figure 2) at ground level were obtained at ~70-80 cm from each side of the canopy per plant (one at each side of the trunk) for nine grapevines per cultivar (n = 18 per cultivar; 36 total per date). Images were analyzed using a customized Matlab ® R2020b code [15,16] (Mathworks Inc., Natick, MA, USA), which is capable of obtaining canopy architecture parameters such as the LAI, effective LAI (LAI e ), crown cover (f f ), canopy cover (f c ), crown porosity (Φ), and Clumping Index (Ω) as described in previous publications [3,13,14,16,17].…”

Section: Canopy Imaging and Digital Measurementsmentioning

confidence: 99%

“…Hence, it is necessary to have rapid, cost-effective, and accurate tools to monitor canopy architecture, potentially serving as a basis for modelling specific berry and wine quality traits. Cover photography has been applied successfully to monitor canopy architecture parameters for eucalyptus trees [24][25][26], apple trees [27], cherry trees [17,28] and grapevines [13,16,29,30]. From the latter studies, a computer application (App) was developed (VitiCanopy), which can use the camera and GPS capabilities of smartphones to obtain digital images and process them to obtain canopy architecture parameters [13].…”

Section: Canopy Architecture Berry Cell Vitality and Machine Learning Modelingmentioning

confidence: 99%

Berry Cell Vitality Assessment and the Effect on Wine Sensory Traits Based on Chemical Fingerprinting, Canopy Architecture and Machine Learning Modelling

Fuentes

Viejo

Hall

et al. 2021

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Berry cell death assessment can become one of the most objective parameters to assess important berry quality traits, such as aroma profiles that can be passed to the wine in the winemaking process. At the moment, the only practical tool to assess berry cell death in the field is using portable near-infrared spectroscopy (NIR) and machine learning (ML) models. This research tested the NIR and ML approach and developed supervised regression ML models using Shiraz and Chardonnay berries and wines from a vineyard located in Yarra Valley, Victoria, Australia. An ML model was developed using NIR measurements from intact berries as inputs to estimate berry cell death (BCD), living tissue (LT) (Model 1). Furthermore, canopy architecture parameters obtained from cover photography of grapevine canopies and computer vision analysis were also tested as inputs to develop ML models to assess BCD and LT (Model 2) and the intensity of sensory descriptors based on visual and aroma profiles of wines for Chardonnay (Model 3) and Shiraz (Model 4). The results showed high accuracy and performance of models developed based on correlation coefficient (R) and slope (b) (M1: R = 0.87; b = 0.82; M2: R = 0.98; b = 0.93; M3: R = 0.99; b = 0.99; M4: R = 0.99; b = 1.00). Models developed based on canopy architecture, and computer vision can be used to automatically estimate the vigor and berry and wine quality traits using proximal remote sensing and with visible cameras as the payload of unmanned aerial vehicles (UAV).