2023
1.
Wang, T.; Sankari, P.; Brown, J.; Paudel, A.; He, L.; Karkee, M.; Thompson, A.; Grimm, C.; Davidson, J.r.; Todorovic, S.
Automatic estimation of trunk cross sectional area using deep learning Proceedings Article
In: Precision agriculture, pp. 491–498, Wageningen Academic Publishers, 2023, ISBN: 978-90-8686-393-8, (Section: 62).
Links | BibTeX | Tags: AI, Labor, Pruning
@inproceedings{wang_62_2023,
title = {Automatic estimation of trunk cross sectional area using deep learning},
author = {Wang, T. and Sankari, P. and Brown, J. and Paudel, A. and He, L. and Karkee, M. and Thompson, A. and Grimm, C. and Davidson, J.r. and Todorovic, S.},
url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_62},
doi = {10.3920/978-90-8686-947-3_62},
isbn = {978-90-8686-393-8},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Precision agriculture},
pages = {491\textendash498},
publisher = {Wageningen Academic Publishers},
note = {Section: 62},
keywords = {AI, Labor, Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Parayil, N.; You, A.; Grimm, C.; Davidson, J.r.
Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator Proceedings Article
In: Precision agriculture, pp. 167–174, Wageningen Academic Publishers, 2023, ISBN: 978-90-8686-393-8, (Section: 19).
Links | BibTeX | Tags: Pruning, Thinning
@inproceedings{parayil_19_2023,
title = {Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator},
author = {Parayil, N. and You, A. and Grimm, C. and Davidson, J.r.},
url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_19},
doi = {10.3920/978-90-8686-947-3_19},
isbn = {978-90-8686-393-8},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Precision agriculture},
pages = {167\textendash174},
publisher = {Wageningen Academic Publishers},
note = {Section: 19},
keywords = {Pruning, Thinning},
pubstate = {published},
tppubtype = {inproceedings}
}
3.
He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic
Bidirectional alignment for domain adaptive detection with transformers Proceedings Article
In: Proceedings of International Conference on Computer Vision, 2023.
Abstract | Links | BibTeX | Tags: Pruning
@inproceedings{noauthor_bidirectional_nodate,
title = {Bidirectional alignment for domain adaptive detection with transformers},
author = {He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic},
url = {https://www.amazon.science/publications/bidirectional-alignment-for-domain-adaptive-detection-with-transformers},
year = {2023},
date = {2023-03-08},
urldate = {2023-03-08},
journal = {Amazon Science},
publisher = {Proceedings of International Conference on Computer Vision},
abstract = {We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…},
keywords = {Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…
2022
4.
You, Alexander; Grimm, Cindy; Davidson, Joseph R.
Optical flow-based branch segmentation for complex orchard environments Proceedings Article
In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9180–9186, arXiv, 2022, (ISSN: 2153-0866).
Abstract | Links | BibTeX | Tags: AI, Labor, Pruning, Thinning
@inproceedings{you_optical_2022,
title = {Optical flow-based branch segmentation for complex orchard environments},
author = {You, Alexander and Grimm, Cindy and Davidson, Joseph R.},
url = {http://arxiv.org/abs/2202.13050},
doi = {10.1109/IROS47612.2022.9982017},
year = {2022},
date = {2022-10-01},
urldate = {2022-10-01},
booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {9180\textendash9186},
publisher = {arXiv},
abstract = {Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically controlling the conditions under which the environment is perceived. In this paper, we train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set.},
note = {ISSN: 2153-0866},
keywords = {AI, Labor, Pruning, Thinning},
pubstate = {published},
tppubtype = {inproceedings}
}
Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically controlling the conditions under which the environment is perceived. In this paper, we train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set.
5.
Alexander You; Nidhi Parayil; Josyula Gopala Krishna; Uddhav Bhattarai; Ranjan Sapkota; Dawood Ahmed; Matthew Whiting; Manoj Karkee; Cindy M. Grimm; Joseph R. Davidson
An autonomous robot for pruning modern, planar fruit trees Proceedings Article
In: arXiv, 2022, (arXiv:2206.07201 [cs]).
Abstract | Links | BibTeX | Tags: AI, Labor, Pruning
@inproceedings{you_autonomous_2022,
title = {An autonomous robot for pruning modern, planar fruit trees},
author = { Alexander You and Nidhi Parayil and Josyula Gopala Krishna and Uddhav Bhattarai and Ranjan Sapkota and Dawood Ahmed and Matthew Whiting and Manoj Karkee and Cindy M. Grimm and Joseph R. Davidson},
url = {http://arxiv.org/abs/2206.07201},
doi = {10.48550/arXiv.2206.07201},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
publisher = {arXiv},
abstract = {Dormant pruning of fruit trees is an important task for maintaining tree health and ensuring high-quality fruit. Due to decreasing labor availability, pruning is a prime candidate for robotic automation. However, pruning also represents a uniquely difficult problem for robots, requiring robust systems for perception, pruning point determination, and manipulation that must operate under variable lighting conditions and in complex, highly unstructured environments. In this paper, we introduce a system for pruning sweet cherry trees (in a planar tree architecture called an upright fruiting offshoot configuration) that integrates various subsystems from our previous work on perception and manipulation. The resulting system is capable of operating completely autonomously and requires minimal control of the environment. We validate the performance of our system through field trials in a sweet cherry orchard, ultimately achieving a cutting success rate of 58%. Though not fully robust and requiring improvements in throughput, our system is the first to operate on fruit trees and represents a useful base platform to be improved in the future.},
note = {arXiv:2206.07201 [cs]},
keywords = {AI, Labor, Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
Dormant pruning of fruit trees is an important task for maintaining tree health and ensuring high-quality fruit. Due to decreasing labor availability, pruning is a prime candidate for robotic automation. However, pruning also represents a uniquely difficult problem for robots, requiring robust systems for perception, pruning point determination, and manipulation that must operate under variable lighting conditions and in complex, highly unstructured environments. In this paper, we introduce a system for pruning sweet cherry trees (in a planar tree architecture called an upright fruiting offshoot configuration) that integrates various subsystems from our previous work on perception and manipulation. The resulting system is capable of operating completely autonomously and requires minimal control of the environment. We validate the performance of our system through field trials in a sweet cherry orchard, ultimately achieving a cutting success rate of 58%. Though not fully robust and requiring improvements in throughput, our system is the first to operate on fruit trees and represents a useful base platform to be improved in the future.
