Evaluation of Computer Vision Systems and Applications to Estimate Trunk Cross-Sectional Area, Flower Cluster Number, Thinning Efficacy and Yield of Apple

by Luis Gonzalez Nieto 1, Anna Wallis 2, Jon Clements 3, Mario Miranda Sazo 4, Craig Kahlke 4, Thomas M. Kon 5 and Terence L. Robinson 1

1 Horticulture Section, School of Integrative Plant Science, Cornell University, Geneva, NY 14456, USA
2 Extension Section, Michigan State University, Grand Rapids, MI 49503, USA
3 UMass Extension Section, University of Massachusetts, Belchertown, MA 01007, USA
4 Cornell Cooperative Extension, Lake Ontario Fruit Program, Albion, NY 14411, USA
5 Mountain Horticultural Crops Research and Extension Center, Department of Horticultural Sciences, North Carolina State University, Mills River, NC 28759, USA

Horticulturae 2023, 9(8), 880;

Precision crop load management of apple requires counting fruiting structures at various times during the year to guide management decisions. The objective of the current study was to evaluate the accuracy of and compare different commercial computer vision systems and computer applications to estimate trunk cross-sectional area (TCSA), flower cluster number, thinning efficacy, and yield estimation. These studies evaluated two companies that offer different vision systems in a series of trials across 23 orchards in four states. Orchard Robotics uses a proprietary camera system, and Pometa (previously Farm Vision) uses a cell phone camera system. The cultivars used in the trials were ‘NY1’, ‘NY2’, ‘Empire’, ‘Granny Smith’, ‘Gala’, ‘Fuji’, and ‘Honeycrisp’. TCSA and flowering were evaluated with the Orchard Robotics camera in full rows. Flowering, fruit set, and yield estimation were evaluated with Pometa. Both systems were compared with manual measurements. Our results showed a positive linear correlation between the TCSA with the Orchard Robotics vision system and manual measurements, but the vision system underestimated the TCSA in comparison with the manual measurements (R2s between 0.5 and 0.79). Read more here…