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Dr. Peter Ako Larbi has been an Assistant Cooperative Extension Specialist in Agricultural Application Engineering with the University of California, Division of Agriculture and Natural Resources (UC ANR), since 2018, and a faculty member with the Department of Biological and Agricultural Engineering at University of California Davis (UC-Davis) since 2019. He is based at the Kearney Agricultural Research and Extension Center in Parlier. As part of his research and extension program, his focus is on developing and promoting best practices for safe, economical, and environmentally sound pesticide application with reduced environmental risks. Prior to joining UC ANR, Dr. Larbi had been an Assistant Professor of Agricultural Systems Technology in the College of Agriculture at Arkansas State University since 2014. He developed an integrated teaching and research program related to agricultural systems technology; developed and managed research in precision agriculture, agricultural machinery systems, remote sensing and sensor technology; and provided service to the university, college, local community and general scientific community. He held a joint appointment in the Division of Agriculture at University of Arkansas. From 2012 to 2014, Dr. Larbi was a postdoctoral research associate at the Center for Precision and Automated Agricultural Systems at Washington State University. From 2011 to 2012, he was a postdoctoral researcher at the University of Florida Citrus Research and Edu
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.
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 StyleAlireza 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 StyleAlireza 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.
Microsoft Excel was recently added to the list of software applications for signal and image processing. The use of Excel as a powerful tool for teaching signal and image data processing techniques as demonstrated in agriculture and natural resource management can be easily adopted for biomedical applications. In the same vein, Excel’s proven utility as a research tool can also be harnessed. This chapter expands the methodology of signal and image formation, visualization, enhancement, and image data fusion using Excel. Different types of techniques used in biomedical imaging are introduced, including: X-ray radiography (X-rays), computerized tomography (CT), ultrasound (U/S), magnetic resonance imaging (MRI), and optical imaging. However, the chapter mainly focuses on optical imaging involving a single spectrum or multiple spectra such as RGB. Specific illustrations of corresponding outputs from different techniques are discussed in the chapter for a better appreciation by the reader.
Peter Ako Larbi; Daniel Asah Larbi; Peter Larbi. Adopting Microsoft Excel for Biomedical Signal and Image Processing. Computer Methods and Programs in Biomedical Signal and Image Processing 2020, 1 .
AMA StylePeter Ako Larbi, Daniel Asah Larbi, Peter Larbi. Adopting Microsoft Excel for Biomedical Signal and Image Processing. Computer Methods and Programs in Biomedical Signal and Image Processing. 2020; ():1.
Chicago/Turabian StylePeter Ako Larbi; Daniel Asah Larbi; Peter Larbi. 2020. "Adopting Microsoft Excel for Biomedical Signal and Image Processing." Computer Methods and Programs in Biomedical Signal and Image Processing , no. : 1.
Microsoft Excel is not considered a typical software for digital image processing and analysis. However, based on its large data handling and graphing capabilities, as well as its widespread usage, it presents a good opportunity for use as a tool for teaching image data processing or use in demonstrations requiring little training. It also lends itself well as a potentially useful research tool that can benefit a wide range of users including those with little or no computer programming knowledge. This article demonstrates a new method which can be adopted for teaching concepts of image processing and analysis, consisting of systematic procedures for implementing typical operations in Excel. Categories of operations demonstrated using this method include image preprocessing, image enhancement, image classification, analysis of change over time, and image data fusion. Examples of outputs resulting from using this new method are discussed in the article. The success of this proposed method is hinged on the availability of the required image data, based on which a simple graphical user interface (GUI) application was developed in MATLAB. That application, RGBExcel or the later RGB2X, extracts RGB image data from image files of any format and file size, and exports to Excel for processing. Deployed as standalone applications, both versions can be installed on a 64-bit windows computer and run without MATLAB. Keywords: Color images, Multispectral imagery, Remote sensing, RGB image data, RGB2X, RGBExcel.
Peter Larbi. Advancing Microsoft Excel’s Potential for Teaching Digital Image Processing and Analysis. Applied Engineering in Agriculture 2018, 34, 263 -276.
AMA StylePeter Larbi. Advancing Microsoft Excel’s Potential for Teaching Digital Image Processing and Analysis. Applied Engineering in Agriculture. 2018; 34 (2):263-276.
Chicago/Turabian StylePeter Larbi. 2018. "Advancing Microsoft Excel’s Potential for Teaching Digital Image Processing and Analysis." Applied Engineering in Agriculture 34, no. 2: 263-276.
Peter Ako Larbi; Cyrus Dean Marbaniang; Kumar Bade; Chin Nee Vong; Peter Larbi. Verification of Temperature Sensor Readings Obtained from Game and Trail Cameras Used for Crop Monitoring. 2017 Spokane, Washington July 16 - July 19, 2017 2017, 1 .
AMA StylePeter Ako Larbi, Cyrus Dean Marbaniang, Kumar Bade, Chin Nee Vong, Peter Larbi. Verification of Temperature Sensor Readings Obtained from Game and Trail Cameras Used for Crop Monitoring. 2017 Spokane, Washington July 16 - July 19, 2017. 2017; ():1.
Chicago/Turabian StylePeter Ako Larbi; Cyrus Dean Marbaniang; Kumar Bade; Chin Nee Vong; Peter Larbi. 2017. "Verification of Temperature Sensor Readings Obtained from Game and Trail Cameras Used for Crop Monitoring." 2017 Spokane, Washington July 16 - July 19, 2017 , no. : 1.
Chin Nee Vong; Peter Ako Larbi; Peter Larbi. Development and Preliminary Prototype Testing of an Agricultural Nozzle Clog Detection Device. 2017 Spokane, Washington July 16 - July 19, 2017 2017, 1 .
AMA StyleChin Nee Vong, Peter Ako Larbi, Peter Larbi. Development and Preliminary Prototype Testing of an Agricultural Nozzle Clog Detection Device. 2017 Spokane, Washington July 16 - July 19, 2017. 2017; ():1.
Chicago/Turabian StyleChin Nee Vong; Peter Ako Larbi; Peter Larbi. 2017. "Development and Preliminary Prototype Testing of an Agricultural Nozzle Clog Detection Device." 2017 Spokane, Washington July 16 - July 19, 2017 , no. : 1.
Mechanical harvest is one promising method to mitigate the labor pressure in fresh-market tree fruit industries. Due to the difficulty in adopting fully mechanical harvesting technologies in most of the current existing canopy architectures, this study aimed to evaluate the feasibility of a mechanical-assist shake-and-catch system to harvest sweet cherries in a large range of commercial orchards. Field evaluation was conducted on four cherry varieties from nine orchards with three canopy architectures. Time distribution in each procedure, and the harvest rate defined as the amount of fruit harvested by a shake-and-catch team (two operators) or a skilled picker in a minute, were analyzed to determine the key determined factors. Time distribution study showed that shaking time accounted for less than 30% of entire operation time, while more than 50% of time was spent on relocation of the shake-and-catch system. Results showed that cherry trees with small pedicel fruit retention force, small canopy size and heavy fruit load had higher harvest rate for mechanical harvesting. The harvest rate could be potentially increased as high as 13.9 times if only the shaking time was considered, which indicated the substantial influence of the processes of catching fruit, relocating fruit catcher and moving ladders. The test results also indicated that the tree architecture had substantial influence on fruit removal efficiency, recovery rate, and fruit damage rate.
Jianfeng Zhou; Long He; Matthew Whiting; Suraj Amatya; Peter A. Larbi; Manoj Karkee; Qin Zhang; Peter Larbi. Field evaluation of a mechanical-assist cherry harvesting system. Engineering in Agriculture, Environment and Food 2016, 9, 324 -331.
AMA StyleJianfeng Zhou, Long He, Matthew Whiting, Suraj Amatya, Peter A. Larbi, Manoj Karkee, Qin Zhang, Peter Larbi. Field evaluation of a mechanical-assist cherry harvesting system. Engineering in Agriculture, Environment and Food. 2016; 9 (4):324-331.
Chicago/Turabian StyleJianfeng Zhou; Long He; Matthew Whiting; Suraj Amatya; Peter A. Larbi; Manoj Karkee; Qin Zhang; Peter Larbi. 2016. "Field evaluation of a mechanical-assist cherry harvesting system." Engineering in Agriculture, Environment and Food 9, no. 4: 324-331.
Peter Larbi. RGBEXCEL : An RGB Image Data Extractor and Exporter for Excel Processing. Signal & Image Processing : An International Journal 2016, 7, 1 -9.
AMA StylePeter Larbi. RGBEXCEL : An RGB Image Data Extractor and Exporter for Excel Processing. Signal & Image Processing : An International Journal. 2016; 7 (1):1-9.
Chicago/Turabian StylePeter Larbi. 2016. "RGBEXCEL : An RGB Image Data Extractor and Exporter for Excel Processing." Signal & Image Processing : An International Journal 7, no. 1: 1-9.
Peter Ako Larbi; Manoj Karkee; Suraj Amatya; Qin Zhang; Matthew David Whiting; Peter Larbi. Field Evaluation of a Modified Mechanical Sweet Cherry Harvester. 2014 ASABE Annual International Meeting 2014, 1 .
AMA StylePeter Ako Larbi, Manoj Karkee, Suraj Amatya, Qin Zhang, Matthew David Whiting, Peter Larbi. Field Evaluation of a Modified Mechanical Sweet Cherry Harvester. 2014 ASABE Annual International Meeting. 2014; ():1.
Chicago/Turabian StylePeter Ako Larbi; Manoj Karkee; Suraj Amatya; Qin Zhang; Matthew David Whiting; Peter Larbi. 2014. "Field Evaluation of a Modified Mechanical Sweet Cherry Harvester." 2014 ASABE Annual International Meeting , no. : 1.
Peter Larbi; Manoj Karkee. Effects of Orchard Characteristics and Operator Performance on Harvesting Rate of a Mechanical Sweet Cherry Harvester. GSTF Journal on Agricultural Engineering 2014, 1, 1 .
AMA StylePeter Larbi, Manoj Karkee. Effects of Orchard Characteristics and Operator Performance on Harvesting Rate of a Mechanical Sweet Cherry Harvester. GSTF Journal on Agricultural Engineering. 2014; 1 (1):1.
Chicago/Turabian StylePeter Larbi; Manoj Karkee. 2014. "Effects of Orchard Characteristics and Operator Performance on Harvesting Rate of a Mechanical Sweet Cherry Harvester." GSTF Journal on Agricultural Engineering 1, no. 1: 1.
Peter A. Larbi; Reza Ehsani; Masoud Salyani; Joe M. Maja; Ashish Mishra; Joao Camargo Neto. Multispectral-based leaf detection system for spot sprayer application to control citrus psyllids. Biosystems Engineering 2013, 116, 509 -517.
AMA StylePeter A. Larbi, Reza Ehsani, Masoud Salyani, Joe M. Maja, Ashish Mishra, Joao Camargo Neto. Multispectral-based leaf detection system for spot sprayer application to control citrus psyllids. Biosystems Engineering. 2013; 116 (4):509-517.
Chicago/Turabian StylePeter A. Larbi; Reza Ehsani; Masoud Salyani; Joe M. Maja; Ashish Mishra; Joao Camargo Neto. 2013. "Multispectral-based leaf detection system for spot sprayer application to control citrus psyllids." Biosystems Engineering 116, no. 4: 509-517.
Peter Ako Larbi; Masoud Salyani; Peter Larbi. Discretization for a spray deposition model: Criteria for temporal and spatial differencing. Computers and Electronics in Agriculture 2013, 97, 35 -39.
AMA StylePeter Ako Larbi, Masoud Salyani, Peter Larbi. Discretization for a spray deposition model: Criteria for temporal and spatial differencing. Computers and Electronics in Agriculture. 2013; 97 ():35-39.
Chicago/Turabian StylePeter Ako Larbi; Masoud Salyani; Peter Larbi. 2013. "Discretization for a spray deposition model: Criteria for temporal and spatial differencing." Computers and Electronics in Agriculture 97, no. : 35-39.
Peter Ako Larbi; Suraj Amatya; Manoj Karkee; Peter Larbi. Characterizing the Response of a Hyperspectral Camera Used in Close Range Imaging under Laboratory Conditions. 2013 Kansas City, Missouri, July 21 - July 24, 2013 2013, 1 .
AMA StylePeter Ako Larbi, Suraj Amatya, Manoj Karkee, Peter Larbi. Characterizing the Response of a Hyperspectral Camera Used in Close Range Imaging under Laboratory Conditions. 2013 Kansas City, Missouri, July 21 - July 24, 2013. 2013; ():1.
Chicago/Turabian StylePeter Ako Larbi; Suraj Amatya; Manoj Karkee; Peter Larbi. 2013. "Characterizing the Response of a Hyperspectral Camera Used in Close Range Imaging under Laboratory Conditions." 2013 Kansas City, Missouri, July 21 - July 24, 2013 , no. : 1.
Lav R. Khot; Reza Ehsani; Gene Albrigo; Peter A. Larbi; Andrew Landers; Joan Campoy; Carl Wellington; Peter Larbi. Air-assisted sprayer adapted for precision horticulture: Spray patterns and deposition assessments in small-sized citrus canopies. Biosystems Engineering 2012, 113, 76 -85.
AMA StyleLav R. Khot, Reza Ehsani, Gene Albrigo, Peter A. Larbi, Andrew Landers, Joan Campoy, Carl Wellington, Peter Larbi. Air-assisted sprayer adapted for precision horticulture: Spray patterns and deposition assessments in small-sized citrus canopies. Biosystems Engineering. 2012; 113 (1):76-85.
Chicago/Turabian StyleLav R. Khot; Reza Ehsani; Gene Albrigo; Peter A. Larbi; Andrew Landers; Joan Campoy; Carl Wellington; Peter Larbi. 2012. "Air-assisted sprayer adapted for precision horticulture: Spray patterns and deposition assessments in small-sized citrus canopies." Biosystems Engineering 113, no. 1: 76-85.
Peter A. Larbi; Masoud Salyani; Peter Larbi. CitrusSprayEx: An expert system for planning citrus spray applications. Computers and Electronics in Agriculture 2012, 87, 85 -93.
AMA StylePeter A. Larbi, Masoud Salyani, Peter Larbi. CitrusSprayEx: An expert system for planning citrus spray applications. Computers and Electronics in Agriculture. 2012; 87 ():85-93.
Chicago/Turabian StylePeter A. Larbi; Masoud Salyani; Peter Larbi. 2012. "CitrusSprayEx: An expert system for planning citrus spray applications." Computers and Electronics in Agriculture 87, no. : 85-93.
Suraj Amatya; Manoj Karkee; Ashok K Alva; Peter Larbi; Bikram Adhikari. Hyperspectral Imaging for Detecting Water Stress in Potatoes. 2012 Dallas, Texas, July 29 - August 1, 2012 2012, 1 .
AMA StyleSuraj Amatya, Manoj Karkee, Ashok K Alva, Peter Larbi, Bikram Adhikari. Hyperspectral Imaging for Detecting Water Stress in Potatoes. 2012 Dallas, Texas, July 29 - August 1, 2012. 2012; ():1.
Chicago/Turabian StyleSuraj Amatya; Manoj Karkee; Ashok K Alva; Peter Larbi; Bikram Adhikari. 2012. "Hyperspectral Imaging for Detecting Water Stress in Potatoes." 2012 Dallas, Texas, July 29 - August 1, 2012 , no. : 1.
Peter Ako Larbi; Masoud Salyani; Peter Larbi. Development of an Expert System for Citrus Spray Applications. Florida Section 2010, 1 .
AMA StylePeter Ako Larbi, Masoud Salyani, Peter Larbi. Development of an Expert System for Citrus Spray Applications. Florida Section. 2010; ():1.
Chicago/Turabian StylePeter Ako Larbi; Masoud Salyani; Peter Larbi. 2010. "Development of an Expert System for Citrus Spray Applications." Florida Section , no. : 1.
Peter Ako Larbi; Masoud Salyani; Peter Larbi. Spray Model to Predict Deposition in Air-Carrier Sprayer Applications. 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010 2010, 1 .
AMA StylePeter Ako Larbi, Masoud Salyani, Peter Larbi. Spray Model to Predict Deposition in Air-Carrier Sprayer Applications. 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. 2010; ():1.
Chicago/Turabian StylePeter Ako Larbi; Masoud Salyani; Peter Larbi. 2010. "Spray Model to Predict Deposition in Air-Carrier Sprayer Applications." 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010 , no. : 1.