Yu Jiang1, Tian Qiu2, Terence Robinson1, Lialiang Cheng3, Kaspar Kuehn3, Kenong Xu1
1Horticulture Section, School of Integrative Plant Science, Cornell AgriTech, Geneva NY | 2School of Electronic and Computer Engineering, Cornell University, Ithaca NY | 3Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca NY
The sustainable growth of the apple industry relies on
managing apple trees with optimal architectural traits,
which significantly influence their growth, fruiting potential,
and environmental interactions. For instance, tree height
affects light exposure to lower branches, impacting fruit yield
and quality, while trunk diameter helps determine the ideal crop
load. Accurate assessment of these traits is crucial for maximizing
orchard productivity and fruit quality.
Traditionally, apple tree traits have been measured manually
using tools like tape measures and calipers. However, these
methods are labor-intensive, subjective, and often inadequate for
capturing the complex architecture essential for fruit production.
Visual inspections might miss subtle differences in branch angles
or lengths that affect fruit distribution and overall yield, and the
intricate structure of trees can make it difficult to take accurate
measurements in the field.
Optical sensing technologies, particularly imaging, are becoming
increasingly popular due to their noninvasive, versatile,
and cost-effective nature (Jiang et al., 2020, Jin et al., 2021, Li et
al., 2014). These technologies provide detailed insights into plant architecture and physiology, driving interest in advanced imaging and machine learning (ML) methods for more precise and efficient trait characterization. Leveraging these technologies can overcome the limitations of traditional methods, leading to a better understanding of tree traits and improved orchard management.
Read full article in Fruit Quarterly, Volume 32, Number 3, Fall 2024: https://nyshs.org/wp-content/uploads/2024/11/NYFQ-BOOK-Fall-2024_v3.pdf