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Publication:  Digital Technologies for Precision Apple Crop Load Management (PACMAN) Part I: Experiences with Tools for Predicting Fruit Set Based on the Fruit Growth Rate Model 

 Anna Wallis, Jon Clements, Mario Miranda Sazo, Craig Kahlke, Karen Lewis, Tom Kon, Luis Gonzalez, Yu Jiang and Terence Robinson 

Reprinted from Fruit Quarterly, Volume 31, Number 1, Spring 2023

Decades of work have demonstrated that PACMAN (Precision Apple Crop load MANagement) is an extremely effective method for successfully managing crop load. Effective crop load management has a direct effect on yield, quality, size, and return bloom, and ultimately an orchard’s profitability. The process involves three management practices: 1) pruning, 2) chemical thinning, and 3) hand thinning, which have been described in detail in previous articles (Robinson et al., 2014a,b). We are continuing to refine recommendations for PACMAN, on a regional basis, as part of a 4-year national project, funded by the USDA-NIFA SCRI. This article is a follow-up to our previous article summarizing earlier work on this project (Robinson et al., 2022). 

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It’s a wrap…

The last of the PACMAN show & tell’s are posted on the WSU CAHNRS YouTube channel here: https://www.youtube.com/playlist?list=PLajA3BBVyv1zc9xkiCSPqj3rEjW2vJ4Yb These now include updates from the PACMAN engineering team, including:

14 – 3-D Imaging and Digital Twin for Specialty Crops (Yu Jiang, Cornell University)

15 – Crop Load Adjustment Based on Early Flower Detection (Long He, Rashmi Sahu, and Paul Heinemann, Penn State)

16 – Improving Apple Harvest with the Latest in AI Yield Estimation for Specialty Crops (Dana Choi, University of Florida)

17 – Moog’s Autononmous Journey: “Agriculture is the Lynch Pin” (Chris Layer and Tom Fischer, Moog, Inc.)