Harry Rogers BSc DPhil MSc

Harry is a postdoctoral researcher at the IBME, working with Professor Alison Noble. Harry completed his PhD at the University of East Anglia, supervised by Professor Beatriz De La Iglesia, as part of the AgriFoRwArdS Centre for Doctoral Training.

His PhD focused on evaluating agricultural precision spraying systems without the use of traditional manual methods. This was achieved by using Computer Vision and Deep Learning to be able to classify and quantify spray deposit volumes on target weeds and non-target crops. The project utilised eXplainable Artificial intelligence to further understand regions of interest Deep Learning model use for prediction. His PhD was completed in collaboration with Syngenta.

• Human-AI Collaboration eXplainable AI (XAI)
• Computer Vision
• Deep Learning
• Robotics

Journal Papers

Rogers, H., De La Iglesia, B., Zebin, T., Cielniak, G., & Magri, B. (2024). Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation. Neural Computing and Applications, 36(32), 20211-20229.

Award Nominated/Winning Papers

Rogers, H., De La Iglesia, B., & Zebin, T. (2023). Evaluating the Use of Interpretable Quantized Convolutional Neural Networks for Resource-Constrained Deployment.

Rogers, H., De La Iglesia, B., Zebin, T., Cielniak, G., & Magri, B. (2023). An Automated Precision Spraying Evaluation System. In Annual Conference Towards Autonomous Robotic Systems (pp. 26-37). Cham: Springer Nature Switzerland.

Conference Papers 

Mayne, V., Rogers, H., Sami, S., & la Iglesia, B. de. (2024). Automating the Clock Drawing Test with Deep Learning and Saliency Maps. In M. F. Santos, J. Machado, P. Novais, P. Cortez, & P. M. Moreira (Eds.), Progress in Artificial Intelligence (pp. 86–97). Cham: Springer Nature Switzerland.

Rogers, H., De La Iglesia, B., & Zebin, T. (2023). Evaluating the Use of Interpretable Quantized Convolutional Neural Networks for Resource-Constrained Deployment.

Rogers, H., De La Iglesia, B., Zebin, T., Cielniak, G., & Magri, B. (2023). An Automated Precision Spraying Evaluation System. In Annual Conference Towards Autonomous Robotic Systems (pp. 26-37). Cham: Springer Nature Switzerland.

Rogers, H., De La Iglesia, B., Zebin, T., Cielniak, G., & Magri, B. (2023). An Agricultural Precision Sprayer Deposit Identification System. In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) (pp. 1-6). IEEE.

Rogers, H., & Fox, C. (2020). An open source seeding agri-robot. In Proceedings of the 3rd UK-RAS Conference. Preprint

Rogers, H., Zebin, T., Cielniak, G., De La Iglesia, B., & Magri, B. (2024). Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition Estimation. arXiv preprint arXiv:2409.16213. • Rogers, H., & Zebin, T. (2022). Explainable Droplet Recognition System for Precision Sprayer Applications. In FARSCOPE CDT Conference.

Rogers, H., Yan, H., Chukwuma, O., Would, O., Elmanzor, E., Rohde, W., … E. Ghalamzan, A. (2021). Robotic Manipulators in Agriculture: A Brief Review. Task-Informed Grasping: Agri-Food Manipulation (TIG-III) Workshop.

Dawson, B., Clawson, G., Rogers, H., & Fox, C. (2021). Extending an Open Source Hardware Agri-Robot with Simulation and Plant Re-identification.

Best Paper Award at 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR).

Nominated Best Application Paper at 25th Towards Autonomous Robotic Systems (TAROS) Conference.

Member of the EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS