Month: November 2025

(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.