EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS Logo. EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS site title logo image.

EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - Benjamin Horner

Benjamin Nicholls

  • University of Lincoln in collaboration with Agrii

Research Interests

Human-robot interaction, machine learning and soft robotics

Publications

Stevenson, R., Paparas, D., Faris, O., Xu, X., Merchant, C., Smith, E., Nicholls, B., & Fox, C. (2025). Open Source Hardware Whisker Sensor. In: Huda, M.N., Wang, M., Kalganova, T. (eds) Towards Autonomous Robotic Systems. TAROS 2024. Lecture Notes in Computer Science, vol 15051. Springer, Cham. [December 2024].

Presentations

“AI cleaning of unstructured soil data” (poster) – AgriFoRwArdS CDT Annual Conference 2024: Robots in Action [July 2024] – Norwich, UK.

“Domain Generalisation for plant/weed detection” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

“AI cleaning of unstructured data for application in models trained on lab-derived and clean datasets” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

“The AgriFoRwArdS CDT Summer School – Responsible adoption (Theme 3) & Phenotyping Perception beyond 2D Colour (Theme 6)” (oral) – Towards Autonomous Robotic Systems (TAROS) 2024 [August 2024] – London, UK.

“Imaging stale seedbeds for weed mapping, monitoring and early season control” (oral) – AgriFoRwArdS Seminar Day and Quarterly Research Progress Meeting [December 2024] – Lincoln, UK.

“Seed Population Modelling” (oral) – AgriFoRwArdS CDT Annual Conference 2025 [May 2025] – Online.

“Baa Force One” (oral) – AgriFoRwArdS CDT Summer School 2025: Going to the Dogs! [June 2025] – Lincoln, UK.

Activities and Outputs

  • Represented the AgriFoRwArdS CDT at the University of Lincoln’s stand at CropTec 2023 – showcasing research and communicating with the public [November 2023].
  • Author of AgriFoRwArdS CDT news article ‘Students attend Croptec 2023‘ [December 2023].
  • Contributor to AgriFoRwArdS CDT news article ‘Peer-to-peer support for the transition to PhD’ [March 2024].
  • Author of AgriFoRwArdS CDT news article ‘Students visit Vertical Farming Lab‘ [April 2024].
  • Demonstrator for University of Lincoln modules CMP9781 Big Data Analytics and Modelling (MSc) and CMP3752 Parallel Programming (third year Computer Science) [October 2024 to September 2025].

About me

I joined the CDT for the opportunity to get exposure to real-world applications of robotics. I’m looking forward to getting to work alongside industry partners to get a better understanding of a problem from the initial concept all the way to the end goal/product.

I studied at the University of Lincoln for my undergraduate degree and I enjoyed it so much here I decided to stay; my favourite aspect of Lincoln is the amount of green spaces there are around the city. 

MSc Project

AI cleaning of unstructured data for application in models trained on lab derived and clean datasets

From an image of the ground featuring seeds and some freshly germinated weeds, remove noise, areas of non-interest, and segment areas of interest. The cleaned images are intended for use on a weed seed classifier as part of my PhD project.

PhD Project

Imaging stale seedbeds for weed mapping, monitoring and early season control

Weed detection systems are an active area of research, and several systems are being brought to market to enable targeted spot spraying systems, that can dramatically reduce the amount of herbicide used (and so reduce the environmental and financial costs). However, these systems typically work on weeds after the crop emerges. This is an important short coming for spot spraying because many key herbicides are applied before the crop emerges (called pre-emergent herbicide). In addition, a key use cases of weed detection is to map weed populations over time to assess management strategies and emerging resistance. By the time the crop and weeds have emerged the majority of weed control actions have already been carried out. Thus, a weed detection system that works in crop can only be used to monitor post-control weed populations, greatly reducing the ability to monitor management effectiveness.
This PhD will develop a machine-vision pipeline to detect weeds in the pre-crop fallow period, known as a the stale-seedbed. Successfully detecting and mapping weeds in the stale-seedbed will provide a direct measure of the underlying seed bank (composition, distribution and abundance). The seedbank is the most important measure of long-term weed populations because the seed bank is the only means of long-term persistence for almost all arable weeds.
This PhD will explore different multi-species classification and detection deep learning architectures for use in unstructured arable stale-seedbeds. It will also develop tools to converting weed detections to spatially referenced maps of the seed bank (e.g. visual odometry, sensor fusion), and from there  optimal spraying maps (e.g. probabilistic planning). Data collection and model validation will be carried out in field.
Ben’s PhD project is being carried out under the primary supervision of Dr Shaun Coutts and in collaboration with Agrii.