Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
Multistate No.
Program Code
Project Start Date
Sep 1, 2020
Project End Date
Aug 31, 2024
Grant Year
Project Director
Robinson, T. L.
Recipient Organization
Performing Department
Non Technical Summary
Controlling the final fruit number on an apple tree is one of the most economically critical management practices in apple growing. Optimizing fruit numbers within a narrow, economically optimum range is currently imprecisely done by pruning, chemical thinning and remedial hand thinning which is very expensive. Our previous work has shown that precisely controlling crop load has large economic benefits.We have previously developed ideas and tactics to precisely control crop load by calculating the optimum fruit number per tree, manually counting buds, flowers and fruits and by using various computer models we have developed (carbon balance model, fruit growth rate model and the pollen tube growth model) to help growers achieve the optimum number of fruits per tree; however, the process is tedious and time consuming.Through this project we will further develop precision crop load management tools consisting of computer models, machine vision, robotics and decision support tools to which will allow apple growers to accurately calculate a target fruit number for each tree and then quickly count flower buds and later fruitlets using machine vision and geo-referenced maps to guide the severity of pruning and later guide bloom and post-bloom chemical thinning, and lastly to guide human workers when hand thinning to maximize crop value.This project directly addresses SCRI priority area number 3 "to improve production efficiency, handling and processing, productivity, and profitability over the long term"using a systems approach of plant physiology, crop management, computer vision, robotics, economics, sociology and extension.
Animal Health Component
Research Effort Categories

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
Goals / Objectives
It is the long-term goal of this project to provide applegrowers with easy to use rules, on-line computer models, computer vision, and automated vehicles to adjust fruit number to an optimum target to maximize crop value and profitability.Project Objectives:1.Develop and disseminate user-friendly computer-based models and comprehensive crop load management strategies to achieve optimal crop load and maximize crop value.2.Develop, demonstrate, and deploy machine vision and robotic tools to accurately count reproductive structures during dormancy, bud break, bloom, and at the fruitlet stage and geo-reference the information for each tree in the orchard.3.Develop autonomous vehicles and end effectors designed for crop load management that can precisely measure and adjust crop load during dormancy, bud break, bloom, and at the fruitlet stage.4.Evaluate the economic and sociological impacts of adopting precision crop load management for sustainable apple production.5.Initiate rapid and effective outreach efforts to increase the adoption of precision crop load management for sustainable apple production.
Project Methods
1) We will perform replicated crop management field trials with the five most important varieties over four seasons in both University apple research orchards and grower orchards in NY, MI, WA, VA, PA, NC and MA. The field research data will be evaluated both horticulturally and economically to define the optimum target number of apples at each of the locations and with important high-value apple varieties. The field trials willdetermine the economic optimum crop load for apple regions. We will also conduct field analysesof imaging for bud quality and removal by pruning or thinning. Field studies will image,mapand predictearly fruitlet drop to precisely manage thinning. Other field studies will study the fruit set mechanism in 'WA38'. In Washington we will study the. idea of managing crop a bee exclusion system for Fuji and WA38.2) We will perform replicated field research and laboratory studies to improve the pollen tube growth model, the carbohydrate model and the fruit growth rate model and extend this information to apple growers. We willdevelop a universal pollen tube growth model. We will. refine the leaf area development portionof the carbohydrate balance model3) We will conduct engineering, fabrication, testing, and then further refinement in iterative fashion of computer vision systems for counting reproductive structures of a variety of apple trees in commercially relevant planting systems and at various times of the season. We will also develop a cloud-based information system for data processing, storage, and communication to then guide human worker actions. This will include algorithm development and then asensing platform. for fruitlet and branch detection. We will then develop a data management system to process. the information from. the sensors. We will also develop anA\augmented HMI (human machine interface) for human workers. We. will then conduct field trials of prototype computer vision systems in grower orchards in the eastern and western USA.4) Wewill conduct engineering, fabrication, testing and further refinement in iterative fashion of autonomous vehicles for collecting geo-referenced information on flower and fruit number on each apple tree in an orchard with varying apple tree shapes and at various times of the season.5) Wewill conduct engineering, fabrication, testing and further refinement in iterative fashion of end effectors to prune or thin flowers or fruitlets of apple trees in a precise manner with varying apple tree shapes and at various times of the season.6) We will conduct economic studies of the impact of precisely controlling crop load and the economic impact of adopting manual methods or computer vision and robotic methods of precisely controlling crop load. This will include. the development of partial crop budget tools to assess economic impacts of adopting precision crop load management. We will studyapple grower behavior to identify effective ways to increase adoption of precision load management strategies. We will study apple grower stress related to load management strategies.7) We willconduct a robust outreach effort to help apple growers nationwide understand the economic impact and need for precisely controlling crop load and then help them adopt either manual or computer vision/robotic methods of precisely controlling crop load. An important part of our outreach effort will be to form and manage an Advisory Group (AG) of apple orchard owners, consultants, and relevant orchard personnel from each major apple growing region (Northwest, Midwest, and East). We will communicate annually via meetings to a national audience of fruit growers, extension educators and nurserymen via the annual conference of the IFTA held in February each year. The extension team will lead grower field days in their respective apple growing regions that will demonstrate the results of this Project to a broader audience. The extension team will conduct or coordinate applied research/demonstration trials in coordination with Project research PI's. The extension team will develop Extension durable products/deliverables as the project gets going and produces results. Technology transfer of Project outputs/deliverables will be a high priority. Products/deliverables may include but not be limited to publications, website, video products, social media, webinars, spreadsheets/apps. Information derived from project results will published in fruit grower magazines such as the Fruit Quarterly magazine which is sent to all NY and MI apple growers and is available on-line to all growers and trade publications including the GoodFruit Grower, the Fruit Grower News and the American Fruit Grower. The entire project team will publish our results in scientific journals to extend information to the worldwide scientific community. Each co-project director plans 3 scientific publications from this project.?