The Data CAMPP project provides an innovative training course with flexible, hands-on learning opportunities spanning key aspects of an automated data gathering pipeline for the critical bioscience setting. "Data CAMPP" refers to the automated Capture, Analysis and Management of data. The course will deliver units covering fundamental and advanced aspects of image analysis, machine learning and data handling. Training units are acco mpanied by downloadable code, exercises and datasets, and some courses support "lab-by-post" project kits (physical hardware and plants) to enable hands-on learning experiences via remote participation.
The course will also offer complementary in-person activities. Artificial Intelligence (AI) is revolutionising agriculture and agronomy. The ability to use sensors for collecting data in the field, glasshouse and/or polytunnel, and to act on that data via automated analysis, shows huge potential. However, taking advantage of these capabilities requires technical prowess that is currently lacking in the majority of UK bioscientists. The widespread ability to use and, indeed develop AI systems exhibiting these functionalities, deployed for practical use in day-to-day bioscience settings, is sadly absent from both academia and industry.
Images are ubiquitous in the biosciences and are a key source of objective, quantitative data. Recent developments in AI-combined with robot-assisted image and other data capture, as well as the availability of small-footprint, relatively low-cost computing devices enable high-throughput acquisition and analysis of data in real-world settings, beyond academic research labs. While the technical facilities exist, the practical knowledge to design and implement them is also required. This is particularly relevant for bioscientists.
The overarching goal of Data CAMPP is to create a unique and timely learning experience for the bioscience community, covering topics from development and placement of robotics in the field, through to management of phenotyping image sets, and good experimental practices for and ethics of machine learning. Data CAMPP will prepare today's bioscientists to lead tomorrow's AI-driven innovations.
DataCampp on FutureLearn
Our course units are available on the FutureLearn online learning platform. Teaching consists of a mix of videos, articles, practicals and quizzes. Each unit addresses a different aspect of data capture and analysis, and each relevant to plant phenotyping.
As we build the units, you may find you wish to follow a path through several units, and you can see some example paths in the figure above. The course is designed such that you can drop in to units as you wish. Some may be more interesting to you than others depending on your experience and role in plant phenotyping. Red-shaded boxes above will include in-person events to support online courses. The Case Studies Workshop will be in person towards the end of the project.
We hope the have the complete course online by Spring 2023.
- Introduction to Image Analysis for Plant Phenotyping (online)
- Introduction to plant phenotyping technologies (expected September 2022)
- Coding for Robotics and Data Capture (expected Summer 2022)
- Machine Learning for Image Data (expected Summer/Autumn 2022)
- Introduction to robotics for bioscientists (expected Summer 2022)
- Data Management (expected Autumn 2022)
- Data Capture in the Lab
- Data Capture in the Field
- Deep Learning Internals
- Experiment Design for Machine Learning
- Affordable Phenotyping
This project is in partnership with The University of Lincoln. Development and delivery of this course is supported by a UKRI Large-scale data training grant MR/V038850/1, “Data CAMPP (Innovative Training in Data Capture, Analysis and Management for Plant Phenotyping)”
Meet the team
To find our more about the staff behind this project, click here.