Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision
Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision
This paper introduces a computer vision system that uses stereo camera images to segment, cluster, and fit ellipses to apple fruitlets for diameter measurement. By associating clusters across days, the system calculates growth rates. Quantitative results from an apple orchard show prediction of abscission rates within 3.5% of the current caliper method with a sixfold speedup and significantly less manual effort. Results also include images captured by a robotic system in the field, with discussion of steps needed for full autonomy.
The authors present a computer vision method using stereo camera images to estimate apple fruitlet diameters and growth rates, reporting within 3.5% error relative to caliper-based measurements and a sixfold speedup in an orchard setting; full autonomy remains a future step.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
times
6
View original evidence
uantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3.5% of the current method with a 6 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps required to make the process fully autonomous.
Open source S4
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
uantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3.5% of the current method with a 6 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps required to make the process fully autonomous.
Open source S4
arXiv:2212.01506v3 Announce Type: replace Abstract: In this paper, we present a computer vision-based approach to measure the sizes and growth rates of apple fruitlets. Measuring the growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops in order to optimize yield. The current practice of obtaining growth rates involves using calipers t
Open source S2
o record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. With images collected by a hand-held stereo camera, our system, segments, clusters, and fits ellipses to fruitlets to measure their diameters. The growth rates are then calculated by temporally associating clustered fruitlets across days. We provide q
Open source S3
Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision
Open source S1
Source：arXiv Robotics — research abstracts · arxiv.org