Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 75 references that are in the map. First posted on arXiv 3 Dec 2022.
Builds on
29 of 75 references.
References · 29
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Graph Attention Networkscited by 8,854
- 2017Mask R-CNNcited by 7,259
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2016Feature Pyramid Networks for Object Detectioncited by 5,021
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2017Neural Message Passing for Quantum Chemistrycited by 3,460
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,277
17 more
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
- 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
- 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 348
- 2020HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matchingcited by 120
- 2018Real-time self-adaptive deep stereocited by 80
- 2022ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matchingcited by 62
- 20223D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attentioncited by 27
- 2020Joint Object Detection and Multi-Object Tracking with Graph Neural Networkscited by 14
- 2020Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusioncited by 13
- 2021A Robust Illumination-Invariant Camera System for Agricultural Applicationscited by 11
- 2021Bumblebee: A Path Towards Fully Autonomous Robotic Vine Pruningcited by 10
- 2020Viewpoint Planning for Fruit Size and Position Estimationcited by 9
- 2018Monocular Camera Based Fruit Counting and Mapping with Semantic Data Associationcited by 8
- 2022Fruit Mapping with Shape Completion for Autonomous Crop Monitoringcited by 8
- 2020Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Surveycited by 7
- 2019Visual Perception and Modelling in Unstructured Orchard for Apple Harvesting Robotscited by 6
- 2020Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systemscited by 2
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2 papers.