Publications

Gao, X. (2023) Artificial Intelligence and aphid counting: Automatically counting aphids using technology. Sugar Beet Review, 91(2), pp. 30-31. [May 2023].

Gao, X., Xue, W., Lennox, C., Stevens, M., & Gao, J. (2023) Advancing Early Detection of Virus Yellows: Developing a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields, Preprint. [August 2023].

Gao, X., Xue, W., Lennox, C., Stevens, M., & Gao, J. (2024) Developing a hybrid convolutional neural network for automatic aphid counting in sugar beet fields, Computers and Electronics in Agriculture, 220, 108910. [May 2024].

Gao, X., Stevens, M., & Cielniak, G. (2024) Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions. The 27th International Conference on Pattern Recognition VAIB Workshop (ICPR2024), Kolkata, India. [December 2024].

Gao, X. (2024) Automatic Detection, Positioning and Counting of Grape Bunches Using Robots, Preprint. [December 2024].

Gao, X., Stevens, M., Cielniak, G. (2025) Counting with Confidence: Accurate Pest Monitoring in Water Traps. Accepted for publication at the 8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL 2025). [October 2025].

Presentations

“Automatic aphid counting based on yellow water pan trap imagery and deep learning” (poster) – AgriFoRwArdS CDT Annual Conference [June 2022] – Lincoln, UK.

“AphidNet: automatic aphid recognition and counting based on field water trap imagery by deep learning” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [January 2023] – Online.

“Automatic Aphid Counting Based on the Yellow Water Pan Trap Imagery and Deep Learning” (poster) – University of Lincoln Postgraduate Research Showcase 2023 [February 2023] – Lincoln, UK.

“The Preparation and dish up of an English Breakfast with Robots” (oral) – AgriFoRwArdS CDT Summer School 2023 [March 2023] – Lincoln, UK.

“AphidNet: automatic aphid recognition and counting based on field water trap imagery by deep learning” (oral) – AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting [July 2023] – Norwich, UK.

“Automatic Aphid Counting Based on The Yellow Water Pan Trap Imagery and Deep Learning” (poster) – Towards Autonomous Robotic Systems (TAROS) 2023 / AgriFoRwArdS CDT Annual Conference 2023 / Joint Robotics CDT Annual Conference 2023 [September 2023] – Cambridge, UK.

“Automatic Aphid Counting Based on Deep Learning in Unstructured Agriculture Environments” (oral) – BeetTech24 [February 2024] – Newmarket, UK.

“Interactive Perception: A heuristic approach to overcoming physical occlusions in tomato picking” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

“Automatic aphid counting based on deep learning and robotics technology in unstructured agriculture environments” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.

“Automatic aphid counting based on deep learning and robotics technology in unstructured agriculture environments” (oral) – AgriFoRwArdS CDT Annual Conference 2024: Robots in Action [July 2024] – Norwich, UK.

Other activities

• Represented the AgriFoRwArdS CDT in the AgriFoRwArdS CDT video [November 2021].
• Represented the AgriFoRwArdS CDT at the University of Lincoln British Science Week Showcase – showcasing research and robotic demonstrations to school age children [March 2022].
• Represented the AgriFoRwArdS CDT at Douglas Bomford Trust Bi-Annual Meeting – showcasing research and robotic demonstrations to delegates [April 2022].
• Represented the AgriFoRwArdS CDT at the University of Lincoln Showcase for the Boston College Institute of Technology – showcasing research and robotics demonstrations [June 2022].
• Represented the AgriFoRwArdS CDT at the University of Lincoln’s stand at the Lincolnshire Show 2023 – showcasing research and robotics demonstrations [June 2023].
• Discussion Panel at the AgriFoRwArdS CDT Quarterly PhD Research Progress Meeting & new student welcome – Discussion topic: Interview with fellow students; about you, your research, & the PhD experience [September 2023].
• Represented the AgriFoRwArdS CDT at the Innovate UK Knowledge Transfer Network Joint Sector Advisory Board – showcasing research and robotic demonstrations [October 2023].
• Author of AgriFoRwArdS CDT news article ‘Spot the robot dog counts aphids with Xumin‘ [January 2024].

About me

My name is Xumin Gao, I come from China. I have studied and did research work at the Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology. My research was mainly focused on computer vision and robot perception.

After graduating from university, I worked in an autonomous vehicle company, and worked on the algorithm development of computer vision, including vehicle recognition, vehicle feature point extraction. Then I worked in an intelligent agricultural technology company. I was mainly responsible for image-based poisonous weed detection and segmentation for autonomous weeding robots and UAVs, as well as satellite imagery segmentation for farmland monitoring.

I am a robot lover. At present, I have made many robots, including dancing robots,  indoor service robots, weeding robots and so on. If you want to see these lovely robots, you can visit this website. In my spare time, I especially like dancing (I can dance at least five different dances), hiking and exploring some natural life. Sometimes, I also like to record my life and feelings by writing.

The reason why I chose to join the AgriFoRwArdS CDT is that this project is especially close to my research area of interest. In addition, the work experience I have makes me realise that intelligent agricultural robots have great potential development space at present. I will be studying my PhD at the University of Lincoln. I look forward to meeting other AgriFoRwArdS CDT members and working with them. At the same time, I believe I will have a good time in Lincoln.

MSc Project

Automatic aphid counting based on yellow water pan trap imagery and deep learning

Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring the number of aphids can prevent the large-scale outbreak of aphids. However, the manual counting of aphids which is commonly used at present is inefficient, and it requires professional staff to complete it. Therefore, this project designs an automatic aphid counting network based on deep learning to replace the manual counting. Moreover, on this basis, we will challenge the common difficulties of automatic aphids counting, including dense distribution of aphids, tiny size of aphids and so on. It can monitor aphids automatically and accurately, so as to protect crop growth and improve crop yields.

PhD Project

Automatic aphid counting based on deep learning and robotics technology in unstructured agriculture environments

Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring and control of their populations are thus critical. However, the manual counting of aphids, which is the most common practice, is labour-intensive and time-consuming. Therefore, the automatic aphid counting has become an urgent demand and research hot topic.

In this project, first, we will design a convolutional neural network (CNN) that automatically counts aphids and adapts to different aphid density distributions, small aphid sizes, and other challenges. Second, to address the issue with current vision-based aphid counting methods, which often undercount aphids in water traps due to occlusions or aphids being submerged in water, we will propose a novel aphid counting method through interactive stirring actions. Additionally, we will further explore the relationship between stirring actions and aphid counting, including factors such as the type and duration of stirring.

Xumin’s PhD project is being carried out in collaboration with the British Beet Research Organisation (BBRO), with primary supervision by Prof Grzegorz Cielniak.