Please see below publications within the agri-food robotics sector, by AgriFoRwArdS CDT Staff and Students, published since the set up of the CDT.


University of Lincoln

To view more publications by University of Lincoln staff and students, visit the Lincoln Repository.

Harman, H., and Sklar, E. (2022) Multi-agent Task Allocation for Fruit Picker Team Formation (Extended Abstract). In: The 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022).
Pearson, S., Camacho‑Villa, C,. Valluru, R., Gaju, O., Rai, M., Gould, I., Brewer, S., and Sklar, E. (2022) Robotics and Autonomous Systems for Net Zero Agriculture. Current Robotics Reports, 3 . pp. 57-64. ISSN 2662-4087
Harman, H., and Sklar, E. (2022) Multi-Agent Task Allocation Techniques for Harvest Team Formation. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection, 13th-15th July 2022, Italy.
Ghalamzan Esfahani, A., Das, G., Gould, I., Zarafshan, P., Rajendran Sugathakumary, V., Heselden, J., Badiee, A., Wright, I., and Pearson, S. (2022) Applications of robotic and solar energy in precision agriculture and smart farming. In: Solar Energy Advancements in Agriculture and Food Production Systems. Elsevier. ISBN 9780323898669, 9780323886253
Del Duchetto, F., and Hanheide, M. (2022) Learning on the Job: Long-Term Behavioural Adaptation in Human-Robot Interactions. IEEE Robotics and Automation Letters, 7 (3). pp. 6934-6941. ISSN 2377-3766
De Silva, R., Cielniak, G., and Gao, J. (2022) Towards Infield Navigation: leveraging simulated data for crop row detection. In: IEEE International Conference on Automation Science and Engineering (CASE).
Pal, A., Das, G., Hanheide, M., Leite, A.C., and From, P. (2022) An Agricultural Event Prediction Framework towards Anticipatory Scheduling of Robot Fleets: General Concepts and Case Studies. Agronomy, 12 (6). ISSN 2073-4395
Pearson, S., Camacho‑Villa, C., Valluru, R., Oorbessy, G., Rai, M., Gould, I., Brewer, S., and Sklar, E. (2022) Robotics and Autonomous Systems for Net Zero Agriculture. AGRICULTURE ROBOTICS Current Robotics Reports, 3 . pp. 57-64.
Li, X., Lloyd, R., Ward, S., Cox, J., Coutts, S. and Fox, C. (2022) Robotic crop row tracking around weeds using cereal-specific features. Computers and Electronics in Agriculture, 197 . ISSN 0168-1699
Bennett, J., Moncur, B., Fogarty, K., Clawson, G., and Fox, C. (2022) Towards Open Source Hardware Robotic Woodwind: an Internal Duct Flute Player. In: International Computer Music Conference, 3-7 July 2022, Limerick, Ireland.
Harman, H., and Sklar, E. (2022) Challenges for Multi-Agent Based Agricultural Workforce Management. In: The 23rd International Workshop on Multi-Agent-Based Simulation (MABS)).
Manning, L., Brewer, S., Craigon, P., Frey, P.J., Gutierrez, A., Jacobs, N., Kanza, S., Munday, S., Sacks, J., and Pearson, S. (2022) Artificial intelligence and ethics within the food sector: developing a common language for technology adoption across the supply chain. Trends in Food Science and Technology . ISSN 0924-2244
Choi, T., Would, O., Salazar-Gomez, A., and Cielniak, G. (2022) Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. In: 2022 IEEE International Conference on Robotics and Automation (ICRA), 23-27 May 2022, Philadelphia (PA), USA.
Le Louedec, J., and Cielniak, G. (2021) Gaussian map predictions for 3D surface feature localisation and counting. In: BMVC.
Choi, T., and Cielniak, G. (2022) Channel Randomisation with Domain Control for Effective Representation Learning of Visual Anomalies in Strawberries. In: AI for Agriculture and Food Systems, 28-2-2022, Virtual.
Montes, H.A., and Cielniak, G. (2022) Multiple broccoli head detection and tracking in 3D point clouds for autonomous harvesting. In: AAAI - AI for Agriculture and Food Systems.
Ghidoni, S., Terreran, M., Evangelista, D., Menegatti, E., Eitzinger, C., Villagrossi, E., Pedrocchi, N., Castaman, N., Malecha, M., Mghames, S., Castri, L., Hanheide, M., and Bellotto, N. (2022) From Human Perception and Action Recognition to Causal Understanding of Human-Robot Interaction in Industrial Environments. In: Ital-IA 2022, 9th-11th February 2022, Online.
Qi, C., Gao, J., Chen, K., Shu, L., and Pearson, S. (2022) Tea Chrysanthemum Detection by Leveraging Generative Adversarial Networks and Edge Computing. Frontiers in plant science . ISSN 1664-462X
Lei, F., Peng, Z., Liu, M., Peng, J., Cutsuridis, V., and Yue, S. (2022) A Robust Visual System for Looming Cue Detection Against Translating Motion. IEEE Transactions on Neural Networks and Learning Systems . pp. 1-15. ISSN 2162-237X
Qi, C., Gao, J., Pearson, S., Harman, H., Chen, K., and Shu, L. (2022) Tea chrysanthemum detection under unstructured environments using the TC-YOLO model. Expert Systems with Applications, 193 . ISSN 0957-4174
Ravikanna, R., Hanheide, M., Das, G., and Zhu, Z. (2021). Maximising availability of transportation robots through intelligent allocation of parking spaces. TAROS2021, November 2021.
Harman, H., and Sklar, E. (2021). A Practical Application of Market-based Mechanisms for Allocating Harvesting Tasks.  In 19th International Conference on Practical Applications of Agents and Multi-Agent Systems, October 2021. Springer.
Pignon, C.P., Fernandes, S.B., Valluru, R., Bandillo, N., Lozano, R., Buckler, E., Gore, M.A., Long, S.P., Brown, P.J., and Leakey, A.D.B. (2021). Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genesPlant Physiology. August 2021.
Ferguson, J.N., Fernandes, S.B., Monier, B., Miller, N.D., Allen, D., Dmitrieva, A., Schmuker, P., Lozano, R., Valluru, R., Buckler, E.S., Gore, M.A., Brown, P.J., Spalding, E.P., and Leakey, A.D.B. (2021). Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. Plant Physiology. July 2021.
Dai, D., Gao, J., Parsons, S., and Sklar, E. (2021). Small datasets for fruit detection with transfer learning. UKRAS21 Conference: Robotics at home Proceedings, 5–6. July 2021.
Polvara, R., Duchetto, F.D., Neumann, G., and Hanheide, M. (2021). Navigate-and-Seek: a Robotics Framework for People Localization in Agricultural Environments. IEEE Robotics and Automation Letters,1–1. July 2021.
Gong, L., Yu, M., Jiang, S., Cutsuridis, V., and Pearson, S. (2021). Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors, 21(13): 4537. July 2021.
Badiee, A., Wallbank, J.R., Fentanes, J.P., Trill, E., Scarlet, P., Zhu, Y., Cielniak, G., Cooper, H., Blake, J.R., Evans, J.G., Zreda, M., Markus, K. and Pearson, S. (2020). Using Additional Moderator to Control the Footprint of a COSMOS Rover for Soil Moisture Measurement. Water Resources Research, 57(6): e2020WR028478. June 2021
Hussain, A., Govaerts, B., Negra, C., Villa, T.C.C., Suarez, X.C., Espinosa, A.D., Fonteyne, S., Gardeazabal, A., Gonzalez, G., Singh, R.G., Kommerell, V., Kropff, W., Saavedra, V.L., Lopez, G.M., Odjo, S., Rojas, N.P., Ramirez-Villegas, J., Loon, J.V., Vega, D., Verhulst, N., Woltering, L., Jahn, M., and Kropff, M. (2021). One CGIAR and the Integrated Agri-food Systems Initiative: From short-termism to transformation of the world's food systems.  PLOS ONE, 16(6): e0252832. June 2021.
Guevara, L., Khalid, M., Hanheide, M., and Parsons, S. (2021). Assessing the probability of human injury during UV-C treatment of crops by robots. In 4th UK-RAS Conference, June 2021. UK-RAS
Zhivkov, T., Gomez, A., Gao, J., Sklar, E., and Parsons, S. (2021). The need for speed: How 5G communication can support AI in the field. In EPSRC UK-RAS Network (2021). UKRAS21 Conference: Robotics at home Proceedings, pages 55–56, June 2021. UK-RAS.
Rose, D.C., Lyon, J., de Broon, A., Hanheide, M., and Pearson, S. (2021). Responsible Development of Autonomous Robots in Agriculture. Nature Food, 2(5): 306–309. May 2021.
Mayoral, J.C., Grimstad, L., From, P.J., and Cielniak, G. (2021). Integration of a Human-aware Risk-based Braking System into an Open-Field Mobile Robot. In IEEE International Conference on Robotics and Automation (ICRA), May 2021. IEEE
Wagner, N., Kirk, R., Hanheide, M., and Cielniak, G. (2021). Efficient and Robust Orientation Estimation of Strawberries for Fruit Picking Applications. In IEEE International Conference on Robotics and Automation (ICRA), May 2021. IEEE
Gomez, A.S., Aptoula, E, Parsons, S., and Bosilj, P. (2021). Deep Regression versus Detection for Counting in Robotic Phenotyping. IEEE Robotics and Automation Letters, 6(2): 2902–2907. April 2021.
Swann, K, Hadley, P, Hadley, M.A., Pearson, S., Badiee, A., and Twitchen, C. (2021). The effect of light intensity and duration on yield and quality of everbearer and June-bearer strawberry cultivars in a LED lit multi-tiered vertical growing system. In IX International Strawberry Symposium, pages 359–366, April 2021.
Yang, F., Shu, L., Yang, Y., Han, G., Pearson, S., and Li, K. (2021). Optimal Deployment of Solar Insecticidal Lamps over Constrained Locations in Mixed-Crop Farmlands. IEEE Internet of Things Journal. March 2021.
Gao, J., Westergaard, J.C., Sundmark, E.H.R., Bagge, M., Liljeroth, E., and Alexandersson, E. (2021). Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning. Knowledge-Based Systems, 214: 106723. February 2021.
Lujak, M., Sklar, E., and Semet, F. (2021). Agriculture fleet vehicle routing: A decentralised and dynamic problem. AI Communications, 34(1): 55–71. February 2021.
Mghames, S., Hanheide, M., and Esfahani, A. G. (2021). Interactive Movement Primitives: Planning to Push Occluding Pieces for Fruit Picking. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), February 2021. Co̧pyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Jagtap, S., Duong, L., Trollman, H., Bader, F., Garcia-Garcia, G., Skouteris, G., Li, J., Pathare, P., Martindale, W., Swainson, M., and Rahimifard, S. (2021). IoT technologies in the food supply chain. In Galanakis, C., editor(s), Food Technology Disruptions. Elsevier, January 2021.
Harman, H., and Sklar, E. (2021). Auction-based Task Allocation Mechanisms for Managing Fruit Harvesting Tasks. In UKRAS21, pages 47–48, 2021.
Hroob, I., Polvara, R., Mellado, S.M., Cielniak, G., and Hanheide, M. (2021). Benchmark of visual and 3D lidar SLAM systems in simulation environment for vineyards. ITowards Autonomous Robotic Systems Conference (TAROS), 2021.
Duong, L.N., Al-Fadhli, M., Jagtap, S., Bader, F., Martindale, W., Swainson, M., and Paoli, A. (2020). A review of robotics and autonomous systems in the food industry: From the supply chains perspective. Trends in Food Science & Technology, 106: 355–364. December 2020.
Thota, M., Swainson, M., Kollias, S., and Leontidis, G. (2020). Multi-Source Domain Adaptation for Quality Control in Retail Food Packaging. Computers in Industry, 123: 103293. December 2020.
Khan, W., Das, G., Hanheide, M., and Cielniak, G. (2020). Incorporating Spatial Constraints into a Bayesian Tracking Framework for Improved Localisation in Agricultural Environments. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2020. IEEE
Montes, H., Louedec, J.L., Cielniak, G., and Duckett, T. (2020). Real-time detection of broccoli crops in 3D point clouds for autonomous robotic harvesting. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10483–10488, October 2020. IEEE/RSJ
Bochtis, D., Benos, L., Lampridi, M., Marinoudi, V., Pearson, S., and Sorensen, C.G. (2020). Agricultural Workforce Crisis in Light of the COVID-19 Pandemic. Sustainability, 12(19): 8212. October 2020.
Ge, Y., Xiong, Y., and From, P. (2020). Symmetry-based 3D shape completion for fruit localisation for harvesting robots. Biosystems Engineering, 197: 188–202. September 2020.
Ponnambalam, V.R., Bakken, M., Moore, R.J.D., Gjevestad, J.G.O., and From, P. (2020). Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields. Sensors, 20(18): 5249. September 2020.
Xiong, Y., Ge, Y., and From, P. (2020). An obstacle separation method for robotic picking of fruits in clusters. Computers and Electronics in Agriculture, 175: 105397. August 2020.
Martindale, W., Duong, L., Hollands, T., and Swainson, M. (2020). Testing the data platforms required for the 21st century food system using an industry ecosystem approach. Science of The Total Environment, 724. July 2020.
Louedec, J.L., Montes, H.A., Duckett, T., and Cielniak, G. (2020). Segmentation and detection from organised 3D point clouds: a case study in broccoli head detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 285–293, June 2020. IEEE
Ponnambalam, V.R., Fentanes, J.P., Das, G., Cielniak, G., Gjevestad, J.G.O., and From, P. (2020). Agri-Cost-Maps ? Integration of Environmental Constraints into Navigation Systems for Agricultural Robot. In 6th International Conference on Control, Automation and Robotics (ICCAR), April 2020. IEEE
Li, X., Fox, C., and Coutts, S. (2020). Deep learning for robotic strawberry harvesting. In UKRAS20, pages 80–82, April 2020. UK-RAS
Louedec, J.L., Li, B., and Cielniak, G. (2020). Evaluation of 3D Vision Systems for Detection of Small Objects in Agricultural Environments. In The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 2020. SciTePress
Kirk, R., Mangan, M., and Cielniak, G. (2020). Feasibility Study of In-Field Phenotypic Trait Extraction for Robotic Soft-Fruit Operations. In UKRAS20 Conference: ?Robots into the real world? Proceedings, pages 21–23, February 2020. UKRAS
Xiong, Y., Ge, Y., Grimstad, L., and From, P. (2020). An autonomous strawberry harvesting robot: Design, development, integration, and field evaluation. Journal of Field Robotics, 37(2): 202–224. February 2020.
Fentanes, J.P., Badiee, A., Duckett, T., Evans, J., Pearson, S., and Cielniak, G. (2020). Kriging based robotic exploration for soil moisture mapping using a cosmic ray sensor. Journal of Field Robotics, 37(1): 122–136. January 2020.
Kirk, R., Cielniak, G., and Mangan, M. (2020). L*a*b*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks. Sensors, 20(1): 275. January 2020.
Bosilj, P., Aptoula, E., Duckett, T., and Cielniak, G. (2020). Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture. Journal of Field Robotics, 37(1): 7–19. January 2020.
Gong, L., Thota, M., Yu, M., Duan, W., Swainson, M., Ye, X., and Kollias, S. (2020). A novel unified deep neural networks methodology for use by date recognition in retail food package image. Signal, Image and Video Processing. 2020.
Jagtap, S., Duong, L., Trollman, H., Bader, F., Garcia-Garci, G., Skouteris, G., Li, J., Pathare, P., Martindale, W., Swainson, M., and Rahimifard, S. (2020). IoT technologies in the food supply chain. In Food Technology Disruptions. Elsevier, 2020.

University of Cambridge

To view more publications by University of Cambridge staff and students, visit the Cambridge Repository

Hardman, D., George Thuruthel, T., and Iida, F. (2022) Manipulation of free-floating objects using Faraday flows and deep reinforcement learning. Scientific reports, 12(335).
Sochacki, G., Hughes, J., and Iida, F. (2022) Sensorized Compliant Robot Gripper for Estimating the Cooking Time of Boil-Cooked Vegetables. International Conference on Intelligent Autonomous Systems, 16, pp. 227-238.
Gilday, K., Hughes, J., and Iida, F. (2022) Sensing, Actuating, and Interacting Through Passive Body Dynamics: A Framework for Soft Robotic Hand Design. Soft Robotics.
Scimeca, L., Hughes, Josie; M.P., He, L., Nanayakkara, T., and Iida, F. (2022) Action augmentation of tactile perception for soft-body palpation. Soft robotics, 9(2), pp.280-292.
Danno, D., Hauser, S., and Iida, F. (2022) Robotic cooking through pose extraction from human natural cooking using openpose. International Conference on Intelligent Autonomous Systems, 16, pp. 288-298.
Sochacki, G., Abdulali, A., and Iida, F. (2022) Mastication-Enhanced Taste-Based Classification of Multi-Ingredient Dishes for Robotic Cooking. Frontiers in Robotics and AI, 9, pp.108-121.
Thelander, M., Landberg, K., Muller, A., Cloarec, G., Cunniffe, N., Huguet, S., Soubigou-Taconnat, L., Brunaud, V., and Coudert, Y. (2022) Apical dominance control by TAR-YUC-mediated auxin biosynthesis is a deep homology of land plants. Current Biology July 2022.
Thuruthel, T.T., Iida, F. (2022) Multimodel Sensor Fusion for Learning Rich Models for Interacting Soft Robots.
Costi, L., Tagliabue, A., Maiolino, P., Clemens, F., and Iida, F. (2022) Magneto-Active Elastomer Filter for Tactile Sensing Augmentation Through Online Adaptive Stiffening. IEEE Robotics and Automation Letters, 7(3), pp.5928-5933.
Costi, L., Maiolino, P., and Iida, F. (2022) Soft Morphing Interface for Tactile Feedback in Remote Palpation. 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 2022, pp.01-06.
Howison, T., Hughes, J., and Iida, F. (2022) Morphological Sensitivity and Falling Behavior of Paper V-Shapes. Artificial Life, 27(3-4), pp.204-219.
Sadati, H., ElDiwiny, M., Nurzaman, S.G., Iida, F., and Nanayakkara, T. (2022) Embodied Intelligence & Morphological Computation in Soft Robotics Community: Collaborations, Coordination, and Perspective. Embodied Intelligence.
Roels, E., Terryn, S., Iida, F., Bosman, A.W., Norvez, S., Clemens, F., Van Assche, G., Vanderborght, B., and Brancart, J. (2022) Processing of Self‐Healing Polymers for Soft Robotics. Advanced Materials, 34(1).
George Thuruthel, T., Gardner, P., and Iida, F. (2022) Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks. Soft Robotics, Apr 2022.
Aubin, C.A., Gorissen, B., Milana, E., Buskohl, P.R., Lazarus, N., Slipher, G.A., Keplinger, C., Bongard, J., Iida, F., and Lewis, J.A. (2022) Towards enduring autonomous robots via embodied energy. Nature, 602, pp.393-402.
Wang, H., Thuruthel, T.G., Gilday, K., Abdulali, A., and Iida, F. (2022) Machine Learning for Soft Robot Sensing and Control: A Tutorial Study. 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS).
Costi, L., Maiolino, P., and Iida, F. (2022) How the Environment Shapes Tactile Sensing: Understanding the Relationship between Tactile Filters and Surrounding Environment. Frontiers in Robotics and AI, July 2022, pp.180.
Hashem, R., and Iida, F. (2022) Embedded Soft Sensing in Soft Ring Actuator for Aiding with the Self-Organisation of the In-Hand Rotational Manipulation. 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 2022, pp.498-503.
Bücker, C., Geissdoerfer, M., and Kumar, M. (2022) 100 practices to foster consumer acceptance in the circular economy. R&D Management Conference 2021: Innovation in an Era of Disruption.
Chen, X., Lawrence, J.M., Wey, L.T., Schertel, L., Jing, Q., Vignolini, S., Howe, C.J., Kar-Narayan, S., and Zhang, J.Z. (2022) 3D-printed hierarchical pillar array electrodes for high-performance semi-artificial photosynthesis. Nature Materials, 21, pp.811-818.
Siddique, S., and Eves-van den Akker, S. (2022) 57 Nematode management through. Integrated Nematode Management: State-of-the-art and visions for the future, 408.
Gielis, J., Shankar, A., and Prorok, A. (2022) A Critical Review of Communications in Multi-Robot Systems.
Ho, W.R., Tsolakis, N., Dawes, T., Dora, M., and Kumar, M. (2022) A Digital Strategy Development Framework for Supply Chains. IEEE Transactions on Engineering Management.
Blumenkamp, J., Morad, S., Gielis, J., Li, Q., and Prorok, A. (2022) A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies. 2022 International Conference on Robotics and Automation (ICRA), pp.8772-8778.
Zhou, H., Genez, T.A.L., Brintrup, A., and Parlikad, A.K. (2022) A hybrid-learning decomposition algorithm for competing risk identification within fleets of complex engineering systems. Reliability Engineering & System Safety, 217.
Petchrompo, S., Coit, D.W., Brintrup, A., Wannakrairot, A., and Parlikad, A.K. (2022) A review of Pareto pruning methods for multi-objective optimization, Computers & Industrial Engineering.
Raymond, A., Malencia, M., Paulino-Passos, G., Prorok, A. (2022) Agree to disagree: Subjective fairness in privacy-restricted decentralised conflict resolution. Frontiers in Robotics and AI, 9.
Che, W., Chaffey, T., and Forni, F. (2022) Analog cross coupled controller for oscillations: modeling and design via dominant system theory.
Thelander, M., Landberg, K., Muller, A.R.J., Cloarec, G., Cunniffe, N., Huguet, S., Soubigou-Taconnat, L., Brunaud, V., and Coudert, Y. (2022) Apical and basal auxin sources pattern shoot branching in a moss, Cold Spring Harbor Laboratory.
Moencks, M., Roth, E., Bohné, T., Romero, D., and Stahre, J. (2022 ) Augmented Workforce Canvas: a management tool for guiding human-centric, value-driven human-technology integration in industry. Computers & Industrial Engineering, 163.
Brion, D.A.J., Shen, M., and Pattinson, S.W. (2022) Automated recognition and correction of warp deformation in extrusion additive manufacturing. Additive Manufacturing. 56.
Pattinson, S., and Brion, D.A.J. (2022) Automated Recognition and Correction of Warp Deformation in Extrusion Additive Manufacturing.
Yong, B.X., and Brintrup, A. (2022) Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection.
Zaplana, I., Hadfield, H., and Lasenby, J. (2022) Closed-form solutions for the inverse kinematics of serial robots using conformal geometric algebra. Mechanism and Machine Theory, 173.
Yong, B.X., and Brintrup, A. (2022) Coalitional Bayesian autoencoders: Towards explainable unsupervised deep learning with applications to condition monitoring under covariate shift. Applied Soft Computing, 123.
Sheng, Y., Liu, Y., Zhang, J., Yin, W., Oztireli, A.C., Zhang, H., Lin, Z., Shechtman, E., and Benes, B. (2022) Controllable Shadow Generation Using Pixel Height Maps.
Wu, T., Zhong, F., Tagliasacchi, A., Cole, F., and Oztireli, C. (2022) D $^ 2$ NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video.
Brintrup, A., Kosasih, E.E., MacCarthy, B.L., and Demirel, G. (2022) Digital supply chain surveillance: concepts, challenges, and frameworks. The Digital Supply Chain, pp.379-396.
Miranda-Villatoro, F.A., Forni, F., and Sepulchre, R.J. (2022) Dissipativity analysis of negative resistance circuits. Automatica, 136.
Yong, B.X., and Brintrup, A. (2022) Do autoencoders need a bottleneck for anomaly detection?.
Churamani, N., Kara, O., and Gunes, H. (2022) Domain-incremental continual learning for mitigating bias in facial expression and action unit recognition. IEEE Transactions on Affective Computing.
Xue, F., Budvytis, I., Reino, D.O., and Cipolla, R. (2022) Efficient Large-scale Localization by Global Instance Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.17348-17357.
Wang, H., Kaddour, J., Liu, S., Tang, J., Kusner, M., Lasenby, J., and Liu, Q. (2022) Evaluating Self-Supervised Learning for Molecular Graph Embeddings.
Bidinger, S.L., Han, S., Malliaras, G.G., and Hasan, T. (2022) Highly stable PEDOT: PSS electrochemical transistors. Applied Physics Letters, 120(7).
Ogbeide, O., Bae, G., Yu, W., Morrin, E., Song, T., Song, W., Li, Y., Su, B., An, K., and Hasan, T. (2022) Inkjet‐Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment. Advanced Functional Materials.
Prorok, A., Kumar, V., Sadler, B., and Sukhatme, G. (2022) Introduction to the Special Section on Resilience in Networked Robotic Systems. IEEE Transactions on Robotics, 38(1).
Tsai, C., Ahn, J.M., and Mortara, L. (2022) Managing platform-based ecosystems in B2B markets–out-bound open innovation perspective. International Journal of Technology Management, 89, pp.139-162.
Calcagno, V., Cunniffe, N.J., Hamelin, F.M. (2022) Metacommunity dynamics and the detection of species associations in co‐occurrence analyses: Why patch disturbance matters, Functional Ecology.
Morad, S., Liwicki, S., Kortvelesy, R., Mecca, R., and Prorok, A. (2022) Modeling Partially Observable Systems using Graph-Based Memory and Topological Priors. Learning for Dynamics and Control Conference, pp.59-73.
Bae, G., Budvytis, I., and Cipolla, R. (2022) Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.2842-2851.
Paletta, Q., Arbod, G., and Lasenby, J. (2022) Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions.
Zuercher, P.D., Bohné, T., Eger, V.M., and Mueller, F. (2022) Optimising virtual reality training in industry using crowdsourcing
Dodik, A., Papas, M., Öztireli, C., and Müller, T. (2022) Path Guiding Using Spatio‐Directional Mixture Models. Computer Graphics Forum, 41(1), pp.172-189.
Baikie, T.K., Wey, L.T., Medipally, H., Reisner, E., Nowaczyk, M.M., Friend, R.H., Howe, C.J., Schnedermann, C., Rao, A., and Zhang, J.Z. (2022) Photosynthesis re-wired on the pico-second timescale.
Kousoulidis, D., and Forni, F. (2022) Polyhedral Estimation of L-1 and L-infinity Incremental Gains of Nonlinear Systems
Zhuang, C., Choudhary, R., and Mavrogianni, A. (2022) Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks. Building and Environment.
Kortvelesy, R., and Prorok, A. (2022) QGNN: Value Function Factorisation with Graph Neural Networks
Zaplana, I., Hadfield, H., and Lasenby, J. (2022) Singularities of serial robots: Identification and distance computation using geometric algebra. Mathematics, 10(12), pp.2068.
Paletta, Q., Hu, A., Arbod, G., Blanc, P., and Lasenby, J. (2022) SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Gama, F., Li, Q., Tolstaya, E., Prorok, A., and Ribeiro, A. (2022) SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.5182-5191.
Gama, F., Li, Q., Tolstaya, E., Prorok, A., and Ribeiro, A. (2022) Synthesizing decentralized controllers with graph neural networks and imitation learning. IEEE Transactions on Signal Processing, 70, pp.1932-1946.
Stoychev, S., and Gunes, H. (2022) The effect of model compression on fairness in facial expression recognition
Proselkov, Y., Herrera, M., Hernandez, M.P., Parlikad, A.K., and Brintrup, A. (2022) The value of information for dynamic decentralised criticality computation. IFAC-PapersOnLine, 55(2), pp.408-413.
Murray-Watson, R.E., and Cunniffe, N. (2022) Tolerant crops increase growers' yields but promote selfishness: how the epidemiology of disease resistant and tolerant varieties affect grower behaviour. bioRxiv.
Kosasih, E.E., Margaroli, F., Gelli, S., Aziz, A., Wildgoose, N., and Brintrup, A. (2022) Towards knowledge graph reasoning for supply chain risk management using graph neural networks. International Journal of Production Research.
Bettini, M., Kortvelesy, R., Blumenkamp, J., and Prorok, A. (2022) VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning.

University of East Anglia

To view more publications by University of East Anglia staff and students, visit the East Anglia Repository.

Hobley, B., Arosio, R., French, G., Bremner, J., Dolphin, T. and Mackiewicz, M. (2021). Semi-Supervised Segmentation for Coastal Monitoring Seagrass Using RPA Imagery. Remote Sensing, 13 (9). ISSN 2072-4292
Blackwell, R.E.Harvey, R.Queste, B.Y. and Fielding, S. (2020). Colour maps for fisheries acoustic echograms. ICES Journal of Marine Science, 77 (2). 826–834. ISSN 1054-3139
Khampuengson, T., Bagnall, T., & Wang, W. In Bramer, M., and Ellis, R. (2020). Developing Ensemble Methods for Detecting Anomalies in Water Level Data. The 22nd International Conference on Big Data Analytics and Knowledge Discovery, pages 145–151. Springer, SVK, December 2020.
Occhibove, F., Chapman, D.S., Mastin, A.J., Parnell, S.S.R., Agstner, B., Mato-Amboage, R., Jones, G., Dunn, M., Pollard, C.R.J., Robinson, J.S., Marzano, M., Davies, A.L., White, R.M., Fearne, A., and White, S.M. (2020). Eco-epidemiological uncertainties of emerging plant diseases: The challenge of predicting Xylella fastidiosa dynamics in novel environments. Phytopathology, 110(11): 1740–1750. November 2020.
French, G.Mackiewicz, M.Fisher, M.Holah, H.Kilburn, R.Campbell, N. and Needle, C. (2020). Deep neural networks for analysis of fisheries surveillance video and automated monitoring of fish discards. ICES Journal of Marine Science, 77 (4). 1340–1353. ISSN 1054-3139
Colmer, J., O'Neill, C.M., Wells, R., Bostrom, A., Reynolds, D., Websdale, D., Shiralagi, G., Lu, W., Lou, Q., Cornu, T.L., Ball, J., Renema, J., Andaluz, G.F., Benjamins, R., Penfield, S., and Zhou, J. (2020). SeedGerm: a cost effective phenotyping platform for automated seed imaging and machine learning based phenotypic analysis of crop seed germination. New Phytologist, 228(2): 778–793. October 2020.
Bauer, A.Bostrom, A.G.Ball, J.Applegate, C.Cheng, T.Laycock, S.Rojas, S.M.Kirwan, J. and Zhou, J. (2019). Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production: AirSurf-Lettuce. Horticulture Research, 2019 (6). pp. 1-12. ISSN 2052-7276
Alkhudaydi, T.Reynolds, D.Griffiths, S.Zhou, J. and De La Iglesia, B. (2019). An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat. Plant Phenomics, 2019 (July). pp. 1-17. ISSN 2643-6515

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