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.?

Progress 09/01/20 to 08/31/21

Target Audience:All commercial apple growers in the USA, agricultural scientists, agricultural engineers, agricultural technology companies worldwide are the primary audience. Changes/Problems: Significant frost/freeze events in NC and MI damaged the apple crop While this resulted in loss and/or complications of some research associated with Objective 1, our team was still able to make progress on all sub-objectives of Objective 1. The protocols for the two multi-location trials (Obj 1A1 and 1A2) will be adjusted to make their execution more feasible. Sensing system integration and evaluation was delayed largely because of logistics challenges caused by the pandemic. The engineering team (including Cornell, Penn State, and MOOG) has discussed thoroughly and proposed solutions for it. Thus, we will conduct the system integration and field evaluation for apple orchards in New York in Year 2. At the same time, we started some development work (e.g., Obj 2h Augmented HMI) in Year 1 to ensure the overall success and progress of the project One PI from Penn State will be moving to another institution but will continue to serve as a PI on the project What opportunities for training and professional development has the project provided?Undergraduate Students In. MI, six undergraduate students (Austin Chase, John Loch, Lenz Blix, Megan Gawn, Alex Deahl and Wesley Banning) worked in Einhorn's lab on this projects.They learned skills in experimental design, measuring plant organ growth and phenotyping buds, fruitlets, and leaves. Students were trained to operate lab instrumentation and record, collate, and enter data. Graduate Students In NY, Mario Maranda Sazo, (Ph.D. candidate) worked with Cheng on the whole tree destructive sampling project. In NY, Tian Qiu, (first year Ph.D. student in Electrical and Computer Engineering) worked with Jiang starting in Jan 2021 and has focused on the use of full-perspective, high resolution 3D point cloud for apple tree characterization. He developed an initial data processing pipeline and investigated several key algorithms for tree segmentation, trunk detection, and trunk diameter measurement in the past year. In NY, Hehong Li, (first year Master of Engineering student in Electrical and Computer Engineering) worked with Jian since September 2021 and has explored the use of an augmented reality (AR) device for improved human machine interaction. She developed an application on the device for real-time data display. In MI, Laura Hillmann (Ph.D. candidate) worked with Einhorn in all areas of the project. She has been trained in Dr. Tom Sharkey's lab (MSU) on sample preparation, extraction techniques and measurement of nonstructural carbohydrates in fruit tissues. Presentations are provided under the section entitled 'Other Products'. In NC, James Larson, (PhD Candidate) worked with Kon on a research project to evaluate a portable Vis/NIR spectrometer to predict fruit abscission after a chemical thinner application and compared this with other methods to predict fruit abscission. He also worked on the project led by Einhorn to evaluate a novel model to predict fruit abscission and on the project to optimize bud density and crop load of Honeycrisp. He presented research on predicting fruit carbohydrate content with near infrared spectroscopy at the annual conference of the American Society of Horticultural Science and at an extension meeting in NC. In VA, Rashmi Sahu is the graduate student working on this project since August 2021. Graduate students Azlan Zahid, Magni Hussain, and Omeed Mirbod also have been contributing to the project with some of their work during 2021, but they were not paid by the project. Technical In NY, Spenser Howden is a research support specialist with Robinson who worked on the field experiments on this project.Kathy Campo a technician did data entry and fruit quality analysis. In NY, Kaspar Kuehn is a technician with Cheng who managed the trees and assisted in tree destructive sampling. Graham O'Donnell, a recent Cornell graduate who assisted in tree destructive sampling and data collection. In MI, Denise Ruwersma and Gail Byler are technicians with Einhorn who contributed to all crop load experiments to include initial selection of trees, measurements of trunks and bud numbers, pruning and hand thinning to target crop levels, leaf area and fruit growth measures (for both crop load experiments and fruit set prediction experiments), harvests and fruit quality analyses In NC, Chris Clavet is a research specialist; Tatiana Zuber is a research technician; Diana Vercillo is a research technician; and Cassie Detrio-Darby is a research technician who worked with Kon on this project. In WA, Zachary Chapman is an Agricultural Research Technologist worked with Musacchi on this project. Jill Dinius is a temporary research field assistant who helped in the field data collection for these experiments.Ryan Sheick is a Research assistant who was actively involved in data collection in all the objectives. In VA, Kenneth Savia is a field research specialist who worked with Sherif and collected and tabulated field data for fruit set, crop load and fruit cracking. Postdoctoral and Professional In NY, Luis Gonzalez is a post doc with Robinson and is responsible for this project. In MI, Mokhles Elsysy is a post doc with Einhorn and has directed and supported all undergraduate students who worked on this project, organized all lab activities related to harvest and evaluation of fruit quality and will statistically analyze data and aid in the development of the prediction model. In WA, Sara Serra is a research associate professor who works with Musacchi and is actively involved in all the objectives coordinating technical staff and collecting and analyzing data. In WA, Tory Schmidt is a Horticultural Associate working at the WTFRC who works with Musacchi and is actively involved in objectives 1A1 and 1A2, coordinating technical staff and collecting data. In VA, Md Tabibul Islam is aPostdoctoral Research Associate who works with Sherif and has assisted in designing the field experiments, flagging trees and analyzing data. Tabibul has received training on different laboratory analyses for fruit physical and chemical attributes (e.g. fruit weight, size, firmness, color, starch index, prix and ethylene content). How have the results been disseminated to communities of interest? Results of this project have been communicated to scientific and grower community through the project website (PACMAN), through a presentation to the national fruit growers conference (IFTA) in February by Robinson and through scientific articles. In addition individual project leaders in each state have communicated results of the project in state and regional meetings and webinars. In NY, Robinson presented concepts of precision crop load management at the winter fruit schools and also presented 12 webinars on crop load management in May and June. In NY, Jiang gave a digital seminar on Plant Morphological Phenotyping: From 2D to 3D on Oct 28, 2021. Audience included faculty members, professional/postdoctoral researchers, and students who are interested in digital agriculture. In MI, Einhorn, made an oral presentation in December 2021 to stakeholders at the Great Lakes Expo in Grand Rapids, MI on the fruit set prediction model which appears very promising after the first year of research and results have received considerable stakeholder interest. A user-friendly model (Excel data sheet template and instructions) will be provided to stakeholders to beta test for the 2022 thinning season. Einhorn also gave a presentation on apple thinning at the 2021 Spring virtual MSU Extension Fruit Education Meetings to Stakeholders. Einhorn was interviewed for a 2021 article by the Editor of the Good Fruit Grower journal describing our proposed research to stakeholders. L. Hillmann gave a presentation at the annual conference of the American Society for Horticultural Science to academic peers. In NC, Kon made six presentations related to this project to stakeholders in NC and one scientific presentation at a professional conference. Stakeholders in NC, SC, and GA have been introduced to the project, provided feedback on project direction, and have had the opportunity to tour research plots via extension programming. Six presentations related to this project were delivered to stakeholders in the southeast US and one scientific presentation at a professional conference. In VA, Sherifposted results from this project to the Virginia Tech Tree Fruit Extension and Outreach Facebook Page. page is managed by Dr. S. Sherif andadministered by Virginia Tech Extension Specialists and Virginia Cooperative Extension Agents to provide information about tree fruit production.1147 people follow this page.Sherif, S,also posted results from this project to his blog. blog website is managed by Dr. S. Sherif and is primarily dedicated to discuss topics pertinent to tree fruit production in Virginia. There is now a total of 352 subscribers to this blog, the majority of them are tree fruit growers, extension agents and agricultural consultants. Several blogs had articles pertinent to apple crop load management. Field research & demonstration trials were established featuring precision crop load management protocols.In NY, MI and VA, two bud-load pruning trials were set up in Western New York, on Honeycrisp and Gala. Additional demonstration projects included: (1) an on-farm trial to fine tune the fruit growth rate model, (2) the use of ATS guided by the pollen tube growth model, (3) an evaluation ofmetamitron, 6-BA, and ACC, and (4)We collaborated with anindustry vendors of precision crop load management tools (Automated Fruit Scouting - AFS) to evaluate and improve anew AI-driven tool (AFS method) to measure fruit growth rates versus the traditional Cornell hand method. In MI we initiated field validation trial of Farm Vision crop load management technologies. Farm Vision is developing a smartphone-based app for detecting and measuring fruitlets. Two sites were established on commercial orchards to validate the technology Field days and grower/industry/research visits were conducted by project personnel featuring precision crop load management technologies.In NY, three presentations on precision crop load management were mad Cornell LOF summer tour organized in Wayne County.The results of the AFS method of measuring fruit growth rates was presented.In MI, an annual summer field day/tour attended by 250 featured precision technologies under development for tree fruit production.In VA,we extended the findings of our research on precision crop load management to Virginia apple growers and stakeholders through fruit schools, orchard meetings, the Tree Fruit Horticulture Updates blog website ( and the Virginia Tech Tree Fruit Extension and Outreach Facebook page ( In WA, Project PI's and Consultant met in Summer 2021 and visited Project trial sites and commercial orchards to discuss commercial crop load practices.Mr. Rod Farrow, Project Producer-Consultant had meetings with project PI Musacchi and Co-PI Schmidt. Project advisory board member Garrett Grubbs, Chelan Fruit,at a Washington Fruit and Produce Company orchard, Dan Plath and project advisory board member Darrin Belton to discuss the commercial use of the AI-based automated fruit recognition and scouting AFS system.A half day was spent with Steve Mantel, owner of Innov8 Ag at the WTFRC-WSU-Innovat8 Ag "Smart Orchard". We focused on the Green Atlas Cartographer and its ability to count buds, map bloom, and calculate bloom density on the fly. We discussed in detail the value proposition of crop load management, including the variation of value proposition across annual activities (pruning, cluster thinning, bloom thinning, green fruit thinning), varieties and geographic locations. In VA,we conducted a state-wide survey to assess the impacts of our extension program on grower's knowledge of different horticulture-related topics, including apple blossom and fruit thinning. When asked about the impact of predictive models for fruit and blossom thinning, > 56% of respondents rated our extension program "beneficial" and "highly beneficial" for their knowledge of the benefits and utilization of the MaluSim model. In contrast, only 29% found that the PTGM model for blossom thinning was beneficial, and 27% rated it "not beneficial". Such a low rating for the PTGM was predicted to some extent given the potential risk of spring frost and the severe damage it can cause to apple blossoms, making the growers unwilling to start any early thinning treatment. What do you plan to do during the next reporting period to accomplish the goals? We will repeat the multi-location (NY, MI, NC and WA) detailed physiological studies to evaluate the effect of climate on crop load management results. The economics team in NY will calculate the economic optimum crop load for each of the four locations in this study. In NY we will determine the degree to which the current MaluSim model fits the dry matter data obtained from our destructive sampling of Honeycrisp on four rootstocks, and then collect shoot growth, leaf area and photosynthesis data to improve the sub-models where lack of fit is observed. In NY, Peck and Yu will supervise a newly hired post-doc in developing apple pollen tube growth rate models using advanced data analyses, such as machine/deep learning. The position is based in the Peck Lab at Cornell University. Dr. Yu Jiang will provide additional project guidance and supervision. In MI, Einhorn will continue the other physiological studies we have startedand will complete all NSC analyses; complete microscopic analyses of vascular tissues of pedicels; complete histological analyses of ovaries, complete modeling of year 1 fruit set predictions from four sites. Make modifications to improve all protocols for 2022. In NC, Koon will continue and expand his research with a Vis/NIR spectrometer to predict fruit abscission with the evaluation of other commercially relevant cultivars and thinning timings to determine if this technology continues to show merit in. Local/regional extension presentations are planned for the next reporting period along with reporting to scientific audiences. In WA, Musacchi will continue studies on WA38 cultivar to optimize thinning and pollination. They will also evaluate the MOOG Data Rover in 2 styles of orchard canopies (formally trained and free form). Moog corporation will provide training data for fruitlet detection for use in FY 2022 growing season. They will also train grower's to tag fruitlets using Microsoft Azure Machine Learning platform. They will support university partners in the implementation of the Data Rover sensing / algorithm efforts for fruitlet detection. They will also Support university partners in determiningaccuracy of fruitlet detection against ground truthing efforts.They will consult with university partners to determine the extensions of existing Data Rover sensor technology for a broader range of the orchard life cycle In NY, Jiang will evaluate the data rover on several orchard tree canopy shapes to determine its robustness to count buds, flowers and fruits in different styles of orchard canopies. In PA, the engineering team will acquire large amounts of images from early flower buds to green fruitlets using the MOOG imaging rover.They willcomplete the tagging of images taken by the MOOG rover, build the deep learning network models, and then test the algorithms for accuracy.They will.demonstrate the MOOG rover with imaging systems in Adams County, PA.They willgive extension presentations at grower meetings (such as Mid-Atlantic Fruit and Vegetable Convention) and to the IFTA conference in Hershey PA to disseminate the research results from the project. They also plan to train graduate student and possibly undergraduate students on development of scientific skills through daily research activities, publications, and presentations. In VA, Sherif will examine the efficacy and safety of materials and ratesfor chemicals blossom thinning on two apple cultivars, 'Honeycrisp' and 'Fuji'.The thinning experiment with GA4+7" will be repeated to validate the resultsof the 2021 season and optimize the rate and application timing of GA4+7 products to reduce the incidence of stem-end cracking in 'Gala' apples. The extension team will organize demonstrations, field days and grower meetings to present results from this project.

What was accomplished under these goals? Impact of this project.Crop load management is the most economically important management practice in apple growing. Growers use pruning, chemical thinning and hand thinning to imprecisely manage crop load. This project conducted detailed physiological studies with several cultivars in four distinct apple growing regions of the US (West, Midwest, Northeast and Southeast) to precisely define the optimum flower bud load and then fruit load to maximize crop value. The horticultural results from these studies are being evaluated in an economic study to define the optimum crop load in economic terms. Once known, the optimum flower bud load can then be achieved through more precise pruning and fruit thinning We have also worked to improve three mathematical models we have previously developed (pollen tube growth model, apple carbohydrate model, and fruit growth rate model) which growers use to help achieve the optimum crop load. This year's work has led to improvements in all three models. We also made progress on developing an alternative simpler model to the fruit growth rate model. A limitation to grower adoption of strategies to precisely control crop load is the difficulty in manually counting the number of fruits on each tree.Through this project we are developing computer vision and autonomous vehicles which can count and georeference the number of flower buds or fruits on each tree. This year we have made significant progress and have a functioning prototype rover which can count the number of buds, flowers or fruitlets on each tree, upload the data to the cloud, process the date and communicate actionable information to orchard workers thought earphones. Major activities and accomplishments. Objective 1 In 2021 we conducted four multi-location (NY, MI, NC, WA) uniform experiments on (a) flower bud density, (b) crop load, (c) spur quality and (d) fruitlet abscission.At each of the four locations each of the four experiments was conducted using 'Gala' and 'Honeycrisp' apple cultivars. Regression analysis showed different slopes and intercepts at each location indicating that at a given crop load, larger fruits are produced in some climates than in other climates.These results will form the basis of an economic analysis of crop load and crop value being conducted by the economics team under objective 4. The project on fruit abscission led by Einhorn (MI) developed a user-friendly alternative to the fruit growth rate model which generated accurate predictions of final fruit set, comparable to those of the current model. In NC, Kon conducted an assessment of a portable Vis/NIR spectrometer to predict fruitlet abscission potential. They achieved greater than 90% accuracy in predicting whether a fruitlet persisted through the drop period. In WA, studies on the fruit set mechanism in 'WA38' were conducted by Musacchi.These studies support the theory of natural shedding of this variety is driven by high competition between fruitlets. In NY, Peck successfully updated the Pollen Tube Growth Model (PTGM) housed in the Network for Environment and Weather Applications (NEWA). The new interface worked really well for those who tried it this spring. The ability to store data is a game-changer for NEWA. In VA, Sherifconducted laboratory and greenhouse experiments to develop pollen tube growth models (PTGM) for two club apple cultivars. These models will be used directly by apple growers to time their blossom thinning sprays. Additionally, the raw data used for generating these models will be incorporated in a bigger dataset for developing a universal PTGM valid for all the commercial apple cultivars. In NY, Cheng worked to improve the MaluSim apple thinning carbohydrate model bycollecting tree growth and dry matter data on 'Honeycrisp' grown on four different rootstocks. At six key developmental stages throughout the growing season we destructively harvested the trees. Objective 2 Engineers at the MOOG corporation developed a system of computer vision to count the number of buds of flowers on a high density apple tree.Fruitlet data images from individual trees was collected throughout 2 season in New York (2019 and 2020).This data was used by MOOG engineers to develop an AI systems to learn to detect flower buds and fruitlets. The system was mounted on an electric ATV and named the "Data Rover".Efficiency of the Data Rover in counting buds at green tip was approximately 75% when correlated against ground truthing manual counts.Efforts to improve the accuracy have shown that significant cost would be associated with incremental improvements. How good is good enough is still in question? This question will be addressed with the advisory committee. A second step in developing a grower friendly system was to develop a way to process and then communicate information gathered by the rover to orchard workers.In 2020 engineers at MOOG developed a demo system of communicating individual tree data pulled from the cloud to field laborers when standing in front of a specific tree. The PSU group developed an on-tree measurement system of apple fruit size using a stereo vision system and neural network models including Faster R-CNN and Mask R-CNN. The system had a mean absolute error ranging from 1.1 to 4.2 mm. A fruitlet growth tracking algorithm was developed and able to correctly match 74% of all detected fruits during the mature fruit growth stage in September and October. In NY, Jiang focused on tree data collection using a high-resolution laser scanner. He found the optimal configuration for particular apple orchards and a general protocol on how to optimize the data acquisition in different orchards. They also developed a QGIS plugin that can show simulated tree information in a geographical information system. Objective 3 The PSU group have developed branch cutting mechanisms to remove branches from apple trees. A three degree-of-freedom (DoF) branch cutting mechanism was developed and tested in an apple orchard. The results indicated that the mechanism could prune branches up to 1 inch at different orientations. A fruit pulling removal device was developed. No significant difference was found on the required fruit removal forces for the fruits with diameter ranging from 10 -30 mm. Objective 4 In NY, Gomez's team began the economic evaluation of the horticultural data produced under objective 1 to determine in the four climates (NY, NC, MI and WA) the optimum flower bud load and optimum fruit load to maximize crop economic value.This effort will be completed for the data from year 1 by January 2022. The economics team also began developing partial fruit production budgets to be used in the future analysis (2023 and 2024) of the cost effectiveness of the technology developed in this project for precisely controlling crop load. Objective 5 In 2021 we organized an Advisory Committee (AC) consisting of 21 grower, nursery, and industry representatives. An initial meeting was held via Zoom on March 30, 2021 where project PI's presented their research objectives and plan of work followed by an open discussion where committee members gave advice and direction to the project leaders A website was created in the first quarter of 2021,, 'PACMAN' hopefully being an easily remembered acronym for Precision Apple Crop load MANagement. The website will be a platform to highlight our activities and results outside of the scientific publication audience. Therefore, the website target audience is grower, industry, University and Extension practitioners. Several field research & demonstration trials were established featuring precision crop load management protocols in NY, MI and VA. In addition,field days and grower/industry/research visits were conducted by project personnel in NY, MI, VA and WA featuring precision crop load management technologies.? ?


  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Allen, W.A., T. Kon, and S.M. Sherif. 2021. Evaluation of blossom thinning spray timing strategies in apple. Horticulturae. 7(9):1-12. https://
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Fazio G, and Robinson T. 2021. Designer rootstocks: the basis for precision management of apple orchards. Acta Hortic. 1314:275-286 DOI: 10.17660/ActaHortic.2021.1314.35.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Robinson, T.L., P. Francescatto and J. Lordan. 2021. Advances in precision crop load management of apple. Acta Hortic. 1314:133-138 DOI: 10.17660/ActaHortic.2021.1314.18.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Roeder S., Serra S. and Musacchi S. (2021). Pollination Period and Parentage Effect on Pollen Tube Growth in Apple Plants 10(8):1618. DOI: 10.3390/plants10081618
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Roeder S., Serra S., and Musacchi S. (2021). Novel Metrics to Characterize In Vitro Pollen Tube Growth Performance of Apple Cultivars. Plants 10(7):1460. DOI: 10.3390/plants10071460.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Yang X, Chen L-S, Cheng L. Leaf Photosynthesis and Carbon Metabolism Adapt to Crop Load in ⿿Gala⿿ Apple Trees. Horticulturae. 2021; 7(3):47.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Zahid, A., Mahmud, M.S., He, L., Heinemann, P., Choi, D., and Schupp, J. 2021. Technological advancements towards developing a robotic pruner for apple trees: A review. Computers and Electronics in Agriculture, 189, 106383.
  • Type: Book Chapters Status: Published Year Published: 2021 Citation: Larson, J.E., T.M. Kon, and A. Malladi. 2021. Apple fruitlet abscission mechanisms. Hort. Rev. 49:243-274. 10.1002/9781119851981.ch5
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Larson, J.E., P. Perkins-Veazie, G. Ma, T.M. Kon, and M.L. Parker. 2021. Predicting apple carbohydrate content with near infrared spectroscopy. HortScience (Abstr.) HortScience 56(9):S94
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Mirbod, O., Choi, D., Heinemann, P., He, L., and Schupp, J. 2021 In-Field Apple Size and Location Tracking Using Machine Vision to Assist Fruit Thinning and Harvest Decision-Making. 2021 ASABE Annual International Meeting, Paper No. 2100831. St. Joseph, Mich.: ASABE.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Zahid, A., Mahmud, M.S. and He, L. 2021. Evaluation of Branch Cutting Torque Requirements Intended for Robotic Apple Tree Pruning. 2021 ASABE Annual International Meeting, Paper No. 2100262. St. Joseph, Mich.: ASABE.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Robinson, T. and L. Gonzalez. 2021. Do bi-axis trees improve apple yield or fruit quality? Fruit Quarterly 29:(2):5-7.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Gonzalez, L. and T.L. Robinson. 2021. Effect of different reflective ground covers on the coloring of apples at harvest. Fruit Quarterly 29:(4):10-15.