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Mr. Kyle Cheung
Department of Biological and Agricultural Engineering, University of California, Davis

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Research Keywords & Expertise

0 Agriculture
0 Machine Learning
0 computer vision
0 Machine Learning and artificial intelligence
0 Disease Classification

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Short Biography

Applying images for the next generation of farm management

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Journal article
Published: 26 October 2020 in Sustainability
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Unmanaged spray drift from orchard pesticide application contributes to environmental contamination and causes significant danger to farmworkers, nearby residential areas, and neighbors’ crops. Most drift control approaches do not guarantee adequate and uniform canopy spray coverage. Our goal was to develop a spray backstop system that could block drifting from the top without any negative impact on spray coverage and on-target deposition. The design included a foldable mast and a shade structure that covered the trees from the top. We used a continuous loop sampling to assess and quantify the effectiveness of spray backstop on drift potential reduction. We also collected leaf samples from different sections of trees to compare on-target deposition and coverage. The results showed that the spray backstop system could significantly (p-Value < 0.01) reduce drift potential from the top (78% on average). While we did not find any statistical difference in overall canopy deposition with and without the backstop system, we observed some improvement in treetops deposition. This experiment’s output suggests that growers may be able to adjust their air-assist sprayers for a more uniform spray coverage without concern about the off-target movement of spray droplets when they employ the spray backstop system.

ACS Style

Alireza Pourreza; Ali Moghimi; Franz Niederholzer; Peter Larbi; German Zuniga-Ramirez; Kyle Cheung; Farzaneh Khorsandi. Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery. Sustainability 2020, 12, 8862 .

AMA Style

Alireza Pourreza, Ali Moghimi, Franz Niederholzer, Peter Larbi, German Zuniga-Ramirez, Kyle Cheung, Farzaneh Khorsandi. Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery. Sustainability. 2020; 12 (21):8862.

Chicago/Turabian Style

Alireza Pourreza; Ali Moghimi; Franz Niederholzer; Peter Larbi; German Zuniga-Ramirez; Kyle Cheung; Farzaneh Khorsandi. 2020. "Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery." Sustainability 12, no. 21: 8862.

Conference paper
Published: 23 April 2020 in Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
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Almond canopy geometry has been shown to correlate with harvest yield, but the existing specialized and expensive equipment used to measure geometric features provides data limited in resolution and must be operated in a narrow time window, challenging its role in precise orchard management. To increase adoption, this study examines novel aerial data collection methods by small unmanned aerial systems (sUAS) and intuitive data processing methods with the goal of improving accuracy and reducing cost, time, and training required for canopy measurements and potential yield estimation.

ACS Style

Kyle Cheung; Alireza Pourreza; Ali Moghimi; German Zuniga-Ramirez. Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards (Conference Presentation). Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V 2020, 11414, 1141408 .

AMA Style

Kyle Cheung, Alireza Pourreza, Ali Moghimi, German Zuniga-Ramirez. Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards (Conference Presentation). Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V. 2020; 11414 ():1141408.

Chicago/Turabian Style

Kyle Cheung; Alireza Pourreza; Ali Moghimi; German Zuniga-Ramirez. 2020. "Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards (Conference Presentation)." Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V 11414, no. : 1141408.