The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion
Open accessAbstract
We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images. Our pipeline works on image streams from a monocular camera, both in natural light, as well as with controlled illumination at night. We first train a Fully Convolutional Network (FCN) and segment video frame images into fruit and non-fruit pixels. We then track fruits across frames using the Hungarian Algorithm where the objective cost is determined from a Kalman Filter corrected Kanade-Lucas-Tomasi (KLT) Tracker. In order to correct the estimated count from tracking process, we combine tracking results with a Structure from Motion (SfM) algorithm to calculate relative 3D locations and size estimates to reject outliers and double counted fruit tracks. We evaluate our algorithm by comparing with ground-truth human-annotated visual counts. Our results demonstrate that our pipeline is able to accurately and reliably count fruits across image sequences, and the correction step can significantly improve the counting accuracy and robustness. Although discussed in the context of fruit counting, our work can extend to detection, tracking, and counting of a variety of other stationary features of interest such as leaf-spots, wilt, and blossom.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For example, crop counting and yield estimation is an important topic of precision agriculture [202], [141].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [141]
In the citing paper
“To address this issue, a detection-tracking-counting based method is proposed [141], which can reject outliers and double-counted fruits.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [141]
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
(1981) · Cited by 11,963
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
Algorithms for the Assignment and Transportation Problems
Munkres, J. R. (1957) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0105003 · Cited by 4,001
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Pérez, T., & McCool, C. (2016) · Sensors · doi:10.3390/s16081222 · Cited by 1,155
Research works printed but not linked: 1
- 10.1109/COASE.2016.7743500 · DOI
Cited by
Sorted by citations
Items 101–135 of 135
Efficient Adaptive Incremental Learning for Fruit and Vegetable Classification
Guo, K., Chen, H., Zheng, Y., Liu, Q., Ren, S., Hu, H., & Liang, J. (2024) · Agronomy · doi:10.3390/agronomy14061275 · Cited by 3
Woodlog Inventory Optimization using Object Detection and Object Tracking
Borkar, V., Mathew, L. T., Patil, B., & Depuru, B. K. (2024) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/ijisrt24jul1393 · Cited by 3
Multi-Robot Deep Reinforcement Learning with Macro-Actions.
Xiao, Y., Hoffman, J., Xia, T., & Amato, C. (2019) · Cited by 3
A Simple Algorithm for Oncidium Orchid Cut Flower Grading with Deep Learning
Tsai, Y.-T., Wu, H. C., & Zhu, S. M. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-30143-9_22 · Cited by 3
Wang, S., Zhang, X., Shen, H., Tian, M., & Li, M. (2022) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrcsara57040.2022.9903925 · Cited by 3
Orange yield estimation using object tracking and 3D reconstruction
Hassan, A., Mumtaz, R., Palade, V., Amin, A., Mahmood, Z., Khan, N., Noman, M., Imran, M., & Wicha, S. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101088 · Cited by 2
Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset
Lei, J., Prabhu, A., Liu, X., Cladera, F., Mortazavi, M., Ehsani, R. J., Chaudhari, P., & Kumar, V. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3588037 · Cited by 2
Self-citationIncremental 3D Crop Model Association for Real-Time Counting in Dense Orchards
Park, D., Ko, K., Pyo, D., & Kang, J. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3662648 · Cited by 2
Wang, Y., Zhang, B., Qu, A., & Gao, Y. (2026) · Information Processing in Agriculture · doi:10.1016/j.inpa.2026.03.013 · Cited by 2
Arellano, C., Sagredo, K., Muñoz, C., & Govan, J. E. (2025) · Agronomy · doi:10.3390/agronomy15040809 · Cited by 3
A Random Forest Based Smart Fruit Counting
Pavithra, R., & Priyadharshini, P. S. (2022) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais53314.2022.9742725 · Cited by 2
Scene Graph Generation With Structured Aspect of Segmenting the Big Distributed Clusters
Rehman, A. U., Mukhtar, H., Saba, T., Riaz, O., Khan, M. U. G., & Bahaj, S. A. (2022) · IEEE Access · doi:10.1109/access.2022.3155652 · Cited by 2
High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment
Zhang, L., Ren, Y., Tao, S., Jia, J., & Gao, W. (2021) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2021.02.003 · Cited by 2
Plant recognition, detection, and counting with deep learning
Pawara, P. (2021) · doi:10.33612/diss.156115978 · Cited by 2
Determination of sprayer parameters for fruit nurseries
Мусаевич, Т. Р., Dzitsoev, A. P., & Ahmad, A. (2024) · Agricultural machinery and technologies · doi:10.22314/2073-7599-2024-18-2-55-60 · Cited by 1
Unified ViT-CNN for few-shot object counting
Quan, K.-A. C., Nguyen, V.-T., Nguyen, T., & Tran, M. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-024-03792-z · Cited by 1
IoT-Based Precision Litchi Tracking and Counting Method Using Gated Metrics
Lu, J., Bao, G., Deng, X., Han, X., Lan, Y., & Wu, H. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3561130 · Cited by 1
Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach
Niu, J., Liu, Z., Wang, S., Huang, J., & Zhao, J. (2025) · Agriculture · doi:10.3390/agriculture15111160 · Cited by 1
Meyer, L., Ardelean, A.-T., Weyrich, T., & Stamminger, M. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247341 · Cited by 6
Nokta Bulutu Verisi Kullanılarak Elma Bahçesinden Meyve Tespiti
Günen, M. A. (2021) · El-Cezeri Fen ve Mühendislik Dergisi · doi:10.31202/ecjse.962269 · Cited by 2
Detection and Tracking of Pinus Radiata Catkins
Song, E., Schofield, S. D., & Green, R. D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25825-1_12 · Cited by 1
Selçuk, T., & Tütüncü, M. N. (2023) · Traitement du signal · doi:10.18280/ts.400546 · Cited by 1
Leveraging Region Growing Algorithm for Fruit Identification and Extraction in Natural Scene Images
Kammar, M., Aradhana, D., & Virupaksha Gouda, R. (2025) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece65353.2025.11035801 · Cited by 1
A State-of-the-Art Survey of Machine Learning in the Agricultural Sector
Mohanasuganthi, N., Kanimozhi, J., Preethi, V., Rohini, M. S., & Swathilakshmi, V. (2022) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai55433.2022.10028908 · Cited by 1
Automatic apple detection in orchards with computer vision and machine learning
Abidine, M. Z. E., Ahmad, A., Dutağacı, H., & Rousseau, D. (2023) · Acta Horticulturae · doi:10.17660/actahortic.2023.1360.6 · Cited by 1
Assigning Apples to Individual Trees in Dense Orchards using 3D Color Point CloudsPreprint
Abidine, M. Z. E., Dutağacı, H., Galopin, G., & Rousseau, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.13721 · Cited by 1
Advanced Learning and Classification Techniques for Agricultural and Field Robotics
Silwal, A., Prahar, T., & Baweja, H. S. (2021) · Agriculture automation and control · doi:10.1007/978-3-030-70400-1_13
Computer vision for plant and animal inventory
Chen, G. (2021) · doi:10.32469/10355/93224
Garbin, S., Gagliardo, A., Terzer, M., Todescato, M., Matt, D. T., & Giusti, A. (2024) · Mechanisms and machine science · doi:10.1007/978-3-031-64553-2_36
A Computational Framework for Crop Yield Estimation and Phenological Monitoring
Altimiras, F. J., Callejas, S., de Ruyt, R., Vidal, N., Reyes, A., Elbo, M., Martí, L., & Sánchez-Pi, N. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-80084-9_14
Jia, J., Sun, H., Wang, Z., Wei, Y., Mei, S., Li, H., Du, Y., Li, X., Du, Y., & Qiao, Z. (2026) · Smart Agricultural Technology · doi:10.1016/j.atech.2026.101925
Geo-Referenced Factor-Graph SLAM for Orchard-Scale 3D Apple Reconstruction and Yield Estimation
Bharti, D., Faria, L. G., Koenigkan, L. V., Gebler, L., Rossi, A. D., & Santos, T. T. (2026) · Agriculture · doi:10.3390/agriculture16070764
Per‐tree citrus count regression from single‐view images via lightweight deep learning
Qu, H., & Zhang, Z. (2026) · Agronomy Journal · doi:10.1002/agj2.70400
Metadata
- Authors
- Liu, X., Chen, S. W., Aditya, S., Sivakumar, N., Dcunha, S., Qu, C., Taylor, C. J., Das, J., & Kumar, V.
- Affiliations
- University of Pennsylvania (US)
- Venue
- 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2018-10
- Pages
- 1045-1052
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2963353662
- Cited by
- 135
- References
- 21
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 18 · 2023: 23 · 2022: 18 · 2021: 22 · 2020: 21 · 2019: 13
- FWCI
- 40.72
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Agriculture (2015-67021-23857)
- University of Pennsylvania
- Washington State University
- National Institute of Food and Agriculture (67021)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- fruit counting, fruit segmentation, object tracking, structure from motion, 3D localization, monocular camera, nighttime imaging, fully convolutional network, Kalman filter, Kanade-Lucas-Tomasi tracker, Hungarian algorithm, outlier removal, double-counting correction, plant phenotyping
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
