# Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision

arXiv: https://arxiv.org/abs/2212.01506 · HTML: https://mlsanity.com/paper/2212.01506/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 75 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching](https://mlsanity.com/paper/2007.12140/) (2020)
- [Real-time self-adaptive deep stereo](https://mlsanity.com/paper/1810.05424/) (2018)
- [ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching](https://mlsanity.com/paper/2204.11700/) (2022)
- [3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention](https://mlsanity.com/paper/2203.10926/) (2022)
- [Joint Object Detection and Multi-Object Tracking with Graph Neural Networks](https://mlsanity.com/paper/2006.13164/) (2020)
- [Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion](https://mlsanity.com/paper/2001.11584/) (2020)
- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Bumblebee: A Path Towards Fully Autonomous Robotic Vine Pruning](https://mlsanity.com/paper/2112.00291/) (2021)
- [Viewpoint Planning for Fruit Size and Position Estimation](https://mlsanity.com/paper/2011.00275/) (2020)
- [Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association](https://mlsanity.com/paper/1811.01417/) (2018)
- [Fruit Mapping with Shape Completion for Autonomous Crop Monitoring](https://mlsanity.com/paper/2203.15489/) (2022)
- [Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey](https://mlsanity.com/paper/2006.11391/) (2020)
- [Visual Perception and Modelling in Unstructured Orchard for Apple Harvesting Robots](https://mlsanity.com/paper/1912.12555/) (2019)
- [Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systems](https://mlsanity.com/paper/2004.08761/) (2020)

## Built on by (most cited first, 2 of 2)

- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)
- [Transformer-Based Spatio-Temporal Association of Apple Fruitlets](https://mlsanity.com/paper/2503.03200/) (2025)

## Abstract (arXiv)

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 to 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 quantitative 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.