Year: 2025

Key Advances in Precision Crop Load Management.

Plan to Attend the Coming Nationwide Zoom Webinar: ‘The Past, The Present, and The Future of PACMAN”

From Mario Miranda Sazo (Cornell University) and the PACMAN Team

Registration Link: https://cornell.zoom.us/webinar/register/WN_f_zjuqCWQp2nM47F2CiM3Q#/registration

Monday Jan. 12, 2026

11:00am-3pm Eastern Standard Time

10:00am-2pm Central Standard Time

9:00am-1pm Mountain Standard Time

08:00am-12pm Pacific Standard Time

Note: All growers are warmly invited to participate in a nationwide PACMAN (Precision Apple Crop Load Management) webinar featuring nationally renowned scientists. After more than five years of scientific research and ground-truth evaluations, the latest PACMAN results will be presented.

The webinar will also highlight new research directions and provide opportunities for collaboration as we plan the next phase of the PACMAN project in the coming years.

Don’t miss this chance to learn about cutting-edge technologies and strategies that can help improve crop load management and orchard efficiency.

Agenda

11:00-11:10am: Introduction to SCRI-PACMAN project – Terence Robinson, CU

11:10-11:30am: Optimum bud and fruit number of HC and Gala – Terence Robinson, CU

11:30-11:50am: Economics of thinning HC and Gala – Mauricio Guerra, CU

11:50-12:30pm: Fruit Growth Rate Model results – Todd Einhorn, MSU; Tom Kon, NCS

12:30-12:50pm: WA-38 crop load management – Stefano Musacchi, WSU

12:50-1:10pm: Pollen Tube Growth Model improvements – Greg Peck, CU

1:10-1:30pm: Engineering Results – Long He, PSU

1:30-2:00pm: GPS and Variable Rate Spraying – Brian Lawrence, Yu Jiang, CU

2:00-2:20pm: Extending the results of PCLM – Jon Clements, UMass

2:20-2:40pm: Discussion

2:40-3:00pm: Future SCRI proposal to continue PCLM – Yu Jiang and Terence Robinson, CU

What Has PACMAN Delivered for U.S. apple growers?: Over the past several years, the USDA-SCRI PACMAN (Precision Apple Crop Load Management) project has brought together scientists, extension educators, growers, and ag-technology partners to address one of the most challenging aspects of apple production: managing crop load precisely to achieve consistent yields, good fruit size, high quality, and strong return bloom.

PACMAN has advanced both the science and on-farm practice of crop load management and helped move apple production towards a more data-driven, season-long approach.

Crop load is a season-long decision: One of PACMAN’s most important outcomes has been redefining crop load management as a continuous process, not a one-time thinning decision. The project identified four critical stages for evaluating crop load: (1) Dormant bud number, (2) Bloom density, (3) Fruit set and early fruitlet development, and (4) Final fruit number and fruit size.

This framework helps apple growers make earlier and lower-risk decisions, especially in seasons with variable weather and uneven bloom.

Stronger Physiological Understanding: PACMAN improved our understanding of how bud load, bloom density, fruit set, and carbohydrate balance interact to determine final fruit size and return bloom. Research confirmed what some U.S. growers already observed in the field: early decisions strongly influence fruit size uniformity, packout, and next year’s crop. Fruit growth rate measurements and carbon balance concepts have helped refine thinning timing and expectations. 

Digital Imaging Tools: What Works and What Doesn’t: A major focus of PACMAN was the evaluation of digital imaging technologies, including ground-based systems and drones, to count and map buds, blooms, and fruit. Trials in commercial orchards showed that: (1) No system is error-free due to canopy structure and occlusion, (2) Imaging tools are effective at showing orchard- and tree-level trends, (3) These tools greatly improve speed and spatial coverage compared to manual counts.

For U.S. growers, the key takeaway is that imaging does not replace experience – but it can support better, more targeted decisions.   

Understanding Orchard Variability: PACMAN demonstrated that crop load varies significantly: (1) Withing individual trees, (2) Between trees, and (3) Across blocks.

Recognizing this variability opens the door to precision management, including targeted pruning, selective hand thinning, and improved labor allocation-especially important given rising labor costs.

Variable-Rate Thinning and Precision Sprays: PACMAN showed that crop load information can guide variable-rate chemical thinning and other precision spray applications. This reduces the risk of over- or under-thinning, improves fruit size consistency, and helps limit unnecessary chemical use – an important consideration for growers.

Models and Field Data Working Together: The project linked fruit growth models, carbon balance concepts, and field measurements with digital data. This integration improved confidence in early thinning decisions, particularly in challenging years when weather conditions affect thinning response.

Strong University-Extension Collaboration: PACMAN’s success was driven by close collaboration among scientists, extensionists, growers, and ag-tech entrepreneurs. Research trials were conducted in research stations and commercial orchards, and results were shared in real time through winter meetings, field days, Zoom webinars, newsletters, and on-farm demonstrations.

What PACMAN Has Made Clear: PACMAN also clarified current limitations. Digital tools are not yet “plug-and-play” for every orchard, and grower expertise remains essential. However, the project clearly showed that precision crop load management is achievable and improving.

Bottom Line for Growers: PACMAN has helped move U.S. apple production from reactive thinning to proactive, precision-based crop load management. As technologies continue to evolve, the PACMAN framework will remain central to improving orchard efficiency, fruit quality, and long-term profitability.

(continued from years 1 and 2)

Jon M. Clements

University of Massachusetts Extension

jon.clements@umass.edu

Poster presentation and abstract, Great Lakes Fruit Workers 2025 Meeting, Grand Rapids, MI. November 2025.

For the third growing season in a row in 2025, I partnered with Outfield Technologies (outfield.xyz) using Unmanned Aerial Vehicles (DJI drones) to map apple orchard blossom density and crop load. Using an upgraded drone (Mavic 3M), flights and mapping were done in three states: Massachsetts, New Hampshire, and Minnesota. Flights were done during bloom (bloom variability), early summer (fruit count), and pre-harvest (calibrated fruit count), although not all flights were done at all timings in all orchards. Mapping was refined down to within panel (between posts) granularity over approximately 30 acres of orchard blocks including Honeycrisp, Gala, SweeTango, Evercrisp, Macoun, Ambrosia, Ludacrisp, and Cripps Pink varieties. Although the “So what?” question is not fully anserwed here, within panel variability mapping is a tool that makes variable rate spraying, hand thinning, and harvest management decisions in-turn making precision apple crop load management more plausible. Thanks to Precision Crop Load Management of Apples: USDA-NIFA-SCRI SREP 2020-51181-32197 and Massachusetts Fruit Growers’ Association to make this demonstration possible.

Progress 09/01/20 to 08/31/21; Progress 09/01/21 to 08/31/22; Progress 09/01/22 to 08/31/23; Progress 09/01/23 to 08/31/24

Source: NY AGRICULTURAL EXPT STATION submitted to NRP

PRECISION CROP LOAD MANAGEMENT FOR APPLES

When Gemini asked to summarize current tab (linked page above):

The document you are viewing, “Precision Crop Load Management for Apples,” is a project report detailing a multi-state research effort funded by the National Institute of Food and Agriculture (NIFA).

The main points of the summary are:

• Goal: To improve the economic profitability of apple production by developing precision crop load management (PCLM) tools. This involves moving beyond expensive, imprecise manual and chemical thinning methods.
• Approach: The project uses a systems approach that integrates plant physiology, crop management, computer vision, robotics, economics, and extension to give growers the ability to accurately calculate and achieve an optimal fruit number per tree.
• Key Technological Accomplishments:
  • Models: Development of a universal Pollen Tube Growth Model (PTGM) and a fruitlet size distribution model to predict final fruit set and guide chemical thinning decisions.
  • Vision/Robotics: Collaboration with commercial technology companies (e.g., Outfield, Vivid Machines) for field-testing machine vision systems (drones, cameras) to count reproductive structures.
  • Automation: Development of an unmanned ground robot for data acquisition and 3D modeling of apple trees, along with robotic end-effectors for branch cutting and fruit pulling.
• Economic Findings: Research determined that the optimum crop load to maximize profit is approximately 9 fruits/cm² TCA for ‘Gala’ and 8 fruits/cm² TCA for ‘Honeycrisp’.
• Outreach: Results are disseminated through the project website PACMAN.extension.org (listed in the document’s content) and through numerous presentations at winter fruit schools, field days, and industry conferences.

The project is currently in its final year, with a projected end date of August 31, 2025.

Mr. Liam Oulette (and Jon Clements), UMass Center for Agriculture, Food, and the Environment 2024 Summer Scholars Poster Symposium, September 9, 2024.

Dr. Terence Robinson on PACMAN (Precision Apple Cropload MANagement). August 19, 2024 at Cornell AgriTech, Geneva, New York, USA. ©2024 Jon Clements and UMass Extension.

How it Works and How it Doesn't: Mark Russell

DISCLAIMER: Not necessarily approved or endorsed by PACMAN

Cropload management is the major must for peak orchard profitability, and a critical, season-defining responsibility of the modern orchard manager. For generations this was achieved through good horticulture plus trial-and-error PGR (plant growth regulator) applications at optimal timings, all couched in the grower’s knowledge of their own block history. Now, in just the last few decades, every aspect of apple production has gone through a process of microscopy that has allowed precision to creep into our vocabulary, not just as a theoretical goal, but as a mathematical destination. Rootstocks have greater dwarfing characteristics, trees have gotten smaller, densities have gotten tighter, canopies have decreased in depth.

And so now we have, finally, user-friendly(ish) methods for predicting potential, current, future, and final cropload levels. These include Malusim, which can be found here, the Einhorn Method, known as the Fruitlet Size Distribution Model, found here, and the overall PACMAN project. So why do I get the feeling that there are so few growers actually doing it?

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