Hello, I'm Dan MacLean and I'm presenting this pre-recorded pitch on behalf of myself and Alex Webb. We're both very sorry that we can't attend in person but we really do have longstanding prior engagements.

Images have been the key data type in biology since it's very beginning. Even in these technologically sophisticated times they present a substantial challenge.

Our project is called Open Pi-Image and we with this we will build the base of an open solution for collecting and processing imagery.

With Open Pi-Image we will develop open image capture hardware for multispectral image analysis of plant morphodynamics based on the Raspberry Pi.

We'll develop fast and lightweight parallelisable image analysis software that will be run on the Raspberry Pi

And we'll develop a resource base to allow other scientists to rebuild what we've done at other sites.

Open source is important. Open source allows open access and continued development by the community.
Low cost computing is important. Every imaging device has its own controller and data capture and processing system, this reduces programming complexity.
And it is cheap. The Pi is £30. The pinoir infra red imaging camera is £20. And the drivers are open source, all operations are available.
This represents flexibility and scalability.

Open Pi image is designed to solve some problems with current image analysis solutions and create new opportunities that modern technologies allow

First up is multi-Spectral image capture capability - different applications benefit from light capture in different parts of the electromagnetic spectrum, e.g luciferase reporter systems, infrared from photochemical quenching. We'll enable this.

Open Pi-image will be a toolset for in-growth chamber imagery. but controlled growth environments generally have limited space and fixed lighting points, these cause problems with subjects self shading when camera equipment is used within the environment. We'll make a tool that can be used in existing experimental setups.

Processing with single controller machines (a design often imposed by commercial constraint) limits throughput for large image sets. Scientists want to be as productive as possible so we'll also develop Open Pi-Image so that image processing can be done in high throughput fashion

Lastly, cost - almost all image analysis currently requires propietary hardware and software pipelines from commercial providers at prohibitive capital and maintenance cost. By doing this on low cost hardware and open-source software, we can start to break that bind.

Ultimately, Open Pi-Image will be a tool not for specialists like me, but for the motivated and capable scientist from any field of biology.

Our first goal will be to develop an in growth chamber lighting and camera rig.

We'll test light filter gel and collection spectra combinations to produce evenly lit, unshaded, high contrast images.

Team Webb will design and test the structures and mechanics of these rigs, they'll optimising light levels, filter type, camera positions and movement geometry specifications. THese'll be tested under brightfield and infrared illumination.

Team Webb will work on automation, in-development prototypes will be built with Lego construction kits and eventually production designs made for e.g 3D printed versions or bespoke components.

Team MacLean will develop and create motor and actuator controller circuitry, implement controller code for automation and deployment on the Raspberry Pi platforms, assist in design and production of 3D printing files for a reproducible version of the developed rig.

We'll also create a new Python middleware library of this code, write documentation for specialists and non-specialists, release code under CC licence to GitHub.

Team MacLean will write software that will run on this rig.

The software will be designed to be used at a high-level by typical scientists with novice level experience to reimplement and modify the pipeline elsewhere.

We'll do this as a small Python language library that encapsulates pipelines composed of the many algorithms in the large Python sci-kit image and NumPy suites.

To reduce computer hardware requirements we will ensure that our analysis pipelines can be parallelised, we won't use complicated network based techniques, instead we'll take a very low-tech distributed processing approach that will be effective over a given number of low cost Raspberry Pis, building in a level of low cost linear scalability,

All code libraries will be released as source to GitHub under CC licence and as easily installed packages through the Python Package Index.

Our last aim will be to create a resource base to communicate our work and really enable workers outside our group to reproduce, improve, modify and return their modifications back to the project.

Code and digital files will be shared through GitHub under Creative Commons licences.

We will produce a white paper on the apparatus, experimental setup and software and submit to bioRxiv as a preprint.

We'll also set up a blog-style series of walkthrough and build instructions in the manner of AdaFruit tutorials.

Data from control and proof of principle experiments will be released and hosted on FigShare.

The groups in this proposal, Team Webb and Team MacLean have expertise that overlaps but is highly complementary, Team Webb are expert in plant cell signalling and systems biology and have great experimental systems reliant on high quality image analysis. Team MacLean are expert in bioinformatics and software engineering with experience in developing open hardware and image analysis pipelines. The Team's open science credentials are excellent and we have a great history of releasing and maintaining Open web-resources, reproducible research documents and in code packaging. We can definitely deliver this project.

For this we're asking for £2350, as outlined in the proposal. The project should also have a life after the aims are achieved and Team MacLean will cover things like site maintenance and rolling bug-fixes and improvements will be covered by Team MacLean as part of TSL core activity, in the manner of our other open-source software projects.

This project fulfills all of the OpenPlant remit and is going to be a keystone for future image analysis in plants

We have two great labs with common interests but distinct of background, based separately in Cambridge and Norwich, who have never worked together before but are very eager to make the project work.

The main deliverable is itself a significant and useful advance to plant labs. Our project provides for impact beyond that: the modular nature of the components we intend to produce and our experience with open source software development mean that our project will facilitate the return of improvements to our project from external workers.

Our project design will set the ground for a growing future community of peers developing and improving the hardware and codebase in an open and easily accessible way.