Increased efficiency and sustainability
Lead Research Organisation:
Earlham Institute
Department Name: UNLISTED
Abstract
Increasing yield, both intrinsic yield and closing of the yield gap, must be achieved with increased efficiency and sustainability with regard to fertiliser and water input. Increased yield and efficiency in both higher and lower yielding environments, in present and future climates, requires genetic improvements in yield traits per se, as well as in tolerance to abiotic and biotic stresses. Here we address two topics with shared objectives across the wheat and other John Innes Centre (JIC), Rothamsted Research (RRes), Earlham Insitute and Quadram Institute Bioscience. Both require intensive high throughput field phenotyping, aided by the newly- established unique RRes and JIC facilities for in-field automated phenotyping, focussed on wheat research and employed to evaluate novel germplasm, including donor germplasm, segregating and TILLING populations. In this project we will further develop our specific automated high throughput field phenotyping methodologies for monitoring wheat performance, the genetic dissection of key traits and the development of improved germplasm.
Organisations
- Earlham Institute, United Kingdom (Lead Research Organisation)
- Intel Corporation, United States (Collaboration)
- International Centre for Maize and Wheat Improvement (CIMMYT) (Collaboration)
- Aberystwyth University, United Kingdom (Collaboration)
- Nanjing Agricultural University (Collaboration)
- Huazhong Agricultural University (Collaboration)
- Syngenta International AG (Collaboration)
- University of East Anglia, United Kingdom (Collaboration)
- University of Nottingham (Collaboration)
- Bayer (Collaboration)
Publications
Bauer A
(2019)
Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production
in Horticulture Research
Bevan MW
(2017)
Genomic innovation for crop improvement.
in Nature
Bourdais G
(2019)
The use of quantitative imaging to investigate regulators of membrane trafficking in Arabidopsis stomatal closure.
in Traffic (Copenhagen, Denmark)
Reynolds D
(2019)
What is cost-efficient phenotyping? Optimizing costs for different scenarios.
in Plant science : an international journal of experimental plant biology
Reynolds D
(2019)
CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management.
in GigaScience
Watson A
(2018)
Speed breeding is a powerful tool to accelerate crop research and breeding
in Nature Plants
Zhou J
(2017)
Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat.
in Plant methods
| Description | For the first year of DFW, we have: (1) jointly published and submitted 5 articles; (2) hosted 14 industrial visits and Skype conferences to set up industrial collaborations with Syngenta, Bayer Crop Sciences, Limagrain, ADAS, and Berry World; (3) engaged with Bayer Crop Science and was awarded a Bayer G4T focused project (only two focus grants globally in 2017); (4) showcased our Agri-Tech innovation in REAP 2017 and KTN/Newton national Agri-Tech innovation conferences; and (5) been invited to present our work in DFW in six international conferences in the UK, China, Netherlands, and Japan. For the second year of DFW, my lab and I have: (1) jointly published 4 journal articles, 2 Chinese journal articles, 1 major conference proceeding; (2) hosted 12 industrial visits and knowledge exchange events to continue industrial collaborations with Syngenta, BASF, ADAS, and G's Growers, as well as Agri-Tech East and IPPN; (3) carried out hybrid wheat research with BASF and was awarded NVidia GPU grant, Syngenta and Intel's cash contribution for CASE PhD studentship; (4) showcased our Agri-Tech innovation in REAP 2018, Royal Norfolk Show, and the Royal Institute of Great Britain; and (5) been invited to present our work in DFW in a number of international conferences in the UK, China, and Germany. |
| Exploitation Route | (1) For academic users, our hardware and software such as CropQuant, AirSurf and CropSight will be freely available for BBSRC's Designing Future Wheat programme, so that a broader plant research and crop breeding community can access them through our online repository at DFW, EI and GitHub. (2) For industrial users, we have been discussing licensing agreement to share our technologies to further developing as well as resolving real world agricultural problems together. |
| Sectors | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Education,Environment,Other |
| URL | http://www.earlham.ac.uk/zhou-group |
| Description | First year: Our cost-effective phenotyping technologies such as CropQuant, SeedGerm and AirSurf have been reported by local media such as Farm UK and EDP (Eastern Daily Press). Also, they have attracted attention by leading bio-tech, breeding and growing companies companies, including Bayer Crop Sciences, Syngenta, Limagrain, ADAS and Berry World Group. For example, CropQuant technology was awarded Bayer's G4T focus grant in 2017; Agri-Pheno (China) has approached us in order to purchase sole licensing of the CropQuant patent in China; ADAS and Limagrain are both working with EI for the Agri-Tech China-UK Newton funding application; and Syngenta is support Ji Zhou for IPA responsive mode application. Second year: CropQuant and CropSight (an IoT based phenotyping management system, see the DFW publication) technologies are being used by BASF for their hybrid wheat programme. AirSurf-lettuce was jointly developed with G's Growers and the deep-learning based algorithm has experimented in G's iceberg lettuce with an estimated 8.3% actual yield increase. CropQuant-Robot has been interviewed and reported by ITV national news together with UEA/Cambridge Uni.'s Agri-Robot DTC bid. Both Agri-Pheno (China) and Nanjing Agricultural university have approached us in order to purchase sole licensing of the CropQuant patent in China, we are in the final discussion with NAU for the sole licensing agreement in China. |
| First Year Of Impact | 2018 |
| Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Education,Environment,Other |
| Impact Types | Societal,Economic |
| Description | A member of the Science Board of the G20 wheat initiative set up by the G20 agricultural ministers to coordinate wheat research across the G20 countries |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Membership of a guideline committee |
| Impact | By coordinating wheat research to address food security issues across the G20 countries, it makes more effect use of the funding by G20 government agencies |
| Description | Clean Growth Innovation Summit - Feeding the world |
| Geographic Reach | National |
| Policy Influence Type | Participation in a national consultation |
| URL | https://ktn-uk.co.uk/events/clean-growth-innovation-summit-technologies-of-the-future |
| Description | BBSRC response mode award |
| Amount | £582,712 (GBP) |
| Funding ID | BB/R007233/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2018 |
| End | 04/2021 |
| Description | China Partnering Awards - Forge a long-term UK-China relationship in phenotyping, Agri-Tech innovation and crop research for Rice and Wheat |
| Amount | £124,448 (GBP) |
| Funding ID | BB/R021376/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2018 |
| End | 03/2021 |
| Description | UK-China Newton Network+ Round 1 - AirSurf for automated image analysis and UAV-deployed remote sensors |
| Amount | £30,000 (GBP) |
| Funding ID | Newton Network+ SM003 |
| Organisation | Newton Fund |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2018 |
| End | 07/2018 |
| Title | CropQuant distributed phenotyping workstations |
| Description | Cost effective automated phenotyping device, CropQuant, is capable of providing continuous and precise measurements of traits that are key to todays crop research, breeding and agronomic practices. The high-frequency and high-precision phenotypic analysis can enable the accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. To manage distributed infield experiments and crop-climate data collection, we have developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable real-time interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Impact | The bio preprint version (https://www.biorxiv.org/content/early/2017/09/01/161547) has been introduced to the public in September 2017. We have utilised the CropQuant technology to enable the acquisition of high-quality sensor- and image-based plant data in the field as well as in greenhouses (e.g. the published Speed Breeding paper). It was presented in a number of Agri-Tech and innovation conferences such as KTN Automation in Agriculture as well as REAP 2017. We have also successfully established collaborations with ADAS and Bayer Crop Science to apply CropQuant in their breeding and crop research programmes. |
| URL | https://github.com/Crop-Phenomics-Group/CropQuant/releases/ |
| Title | CropSight - an automated data collation, storage, and phenotyping management system |
| Description | CropSight is a PHP and SQL based server platform, which provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralised data management. Data transfer and annotation are accomplished automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSight system, which synchronise daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance whilst different experiments |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | CropSight is a scalable and open-source information management system that can be used to maintain and collate important crop performance and microclimate information. Big data captured by diverse technologies known collectively as the Internet of Things (IoT) is extremely difficult to calibrate, annotate and aggregate. This presents a major challenge for plant scientists trying to understand the dynamics between crop performance, genotypes and environmental factors and for agronomists and farmers monitoring crops in fluctuating agricultural conditions. The new system developed by researchers from the Earlham Institute, John Innes Centre, and University of East Anglia (UEA) provides near real time environmental and crop growth monitoring. |
| URL | https://github.com/Crop-Phenomics-Group/CropSight |
| Title | AirSurf - a deep learning based aerial image analytic platform |
| Description | Derived from DFW's aerial image analysis work, AirSurf-Lettuce is an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. Utilising ultra-large normalized difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers' precise harvest strategies and marketability estimates before the harvest. |
| Type Of Material | Data analysis technique |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. We collaborate with the UK's second largest growers, G's Growers, and developed AirSurf-Lettuce that can measure millions of lettuces in the field, which has also helped the firm to improve the actual yield of iceberg lettuce in real-world agricultural practices. |
| URL | https://github.com/Crop-Phenomics-Group/AirSurf-Lettuce/releases |
| Title | CropSight - a crop phenotyping management system |
| Description | As a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future. |
| Type Of Material | Data handling & control |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | CropSight can enable the accessibility of locally in the field through smart devices and remotely in an office using a personal computer. It has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future. We have used the CropSight system in our collaboration with BASF and Bayer Crop Science. |
| URL | https://github.com/Crop-Phenomics-Group/CropSight |
| Description | A meeting between CIMMYT and DFW funded by BMGF to discuss collaboration projects |
| Organisation | International Centre for Maize and Wheat Improvement (CIMMYT) |
| Country | Mexico |
| Sector | Charity/Non Profit |
| PI Contribution | I organised a meeting funded by Bill and Melinda Gates Foundation brought together members of the BBSRC's coordinated wheat programme (Designing Future Wheat) with members of CIMMYT (who breed wheat for the resource poor in the developing world), discuss potential opportunities for interaction. These opportunities are taken forward by writing proposals for Newton , GCRF or IWYP funding calls |
| Collaborator Contribution | See above |
| Impact | This interaction is still ongoing between members of BBSRC's coordinated wheat programme (Designing Future Wheat) and researchers within CIMMYT with proposals being written for IWYP and Newton calls |
| Start Year | 2018 |
| Description | Awarded Bayer G4T Focus grant for hybrid wheat research, only two were funded globally in 2017 |
| Organisation | Bayer |
| Department | Bayer CropScience Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Utilising the CropQuant technology to develop novel image-based machine learning technologies to enable trait measurements of spikes per unit area, spikelet number, and anther extrusion for hybrid wheat seed production breeding at Bayer. The Zhou lab is the sole academic partner on this grant. My lab will provide (1) the improved CropQuant technology to collect high-quality wheat growth images in the greenhouse and the field; (2) advanced image analysis and machine learning algorithms to detect anther extrusion over time; (3) a deep learning based analysis solution to quantify spikes per unit area and spikelet/spike numbers in field trials. |
| Collaborator Contribution | Dr M. Schmolke and Dr M. Kerns at the European Wheat Breeding Centre, Crop Science Division, Bayer AG, will support the awarded project using (1) male and female breeding lines for hybrid seed production at Bayer; (2) the deployment of hardware and software toolkit for Breeding & Trait Develop in the hybrid wheat seed production; (3) the integration of this project in the breeding programme at Bayer. |
| Impact | This project will provide a unique opportunity to incorporate genetics, computing sciences (computer vision, deep learning, remote sensing, and growth modelling), and breeding in one multidisciplinary R&D project. The project is just initiated in Jan 2018. |
| Start Year | 2018 |
| Description | China-UK Plant Phenomics Centre |
| Organisation | Aberystwyth University |
| Department | Institute of Biological, Environmental and Rural Sciences (IBERS) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Coordinated the MoU for jointly establishing a China-UK Plant Phenomics Centre (CUPPC) between NANJING AGRICULTURAL UNIVERSITY (China), HUAZHONG AGRICULTURAL UNIVERSITY (China), Aberystwyth UNIVERSITY (IBERS, UK), Earlham institute (BBSRC UK), Rothamsted Research (Plant Sciences Dept., BBSRC UK), University of East Anglia (UK), and University of Nottingham (UK). |
| Collaborator Contribution | Signed MoU as well as initiated research visits between the seven research organisations. |
| Impact | The establishment of the China-UK Plant Phenomics Centre (CUPPC) in Nanjing and Wuhan. The MoU signed between seven leading research organisations in China and the UK. The detailed research agreement is being signed between NAU and UEA/EI. The first NAU-NRP research workshop at Norwich Research Park in Jan 2019. |
| Start Year | 2018 |
| Description | China-UK Plant Phenomics Centre |
| Organisation | Huazhong Agricultural University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Coordinated the MoU for jointly establishing a China-UK Plant Phenomics Centre (CUPPC) between NANJING AGRICULTURAL UNIVERSITY (China), HUAZHONG AGRICULTURAL UNIVERSITY (China), Aberystwyth UNIVERSITY (IBERS, UK), Earlham institute (BBSRC UK), Rothamsted Research (Plant Sciences Dept., BBSRC UK), University of East Anglia (UK), and University of Nottingham (UK). |
| Collaborator Contribution | Signed MoU as well as initiated research visits between the seven research organisations. |
| Impact | The establishment of the China-UK Plant Phenomics Centre (CUPPC) in Nanjing and Wuhan. The MoU signed between seven leading research organisations in China and the UK. The detailed research agreement is being signed between NAU and UEA/EI. The first NAU-NRP research workshop at Norwich Research Park in Jan 2019. |
| Start Year | 2018 |
| Description | China-UK Plant Phenomics Centre |
| Organisation | Nanjing Agricultural University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Coordinated the MoU for jointly establishing a China-UK Plant Phenomics Centre (CUPPC) between NANJING AGRICULTURAL UNIVERSITY (China), HUAZHONG AGRICULTURAL UNIVERSITY (China), Aberystwyth UNIVERSITY (IBERS, UK), Earlham institute (BBSRC UK), Rothamsted Research (Plant Sciences Dept., BBSRC UK), University of East Anglia (UK), and University of Nottingham (UK). |
| Collaborator Contribution | Signed MoU as well as initiated research visits between the seven research organisations. |
| Impact | The establishment of the China-UK Plant Phenomics Centre (CUPPC) in Nanjing and Wuhan. The MoU signed between seven leading research organisations in China and the UK. The detailed research agreement is being signed between NAU and UEA/EI. The first NAU-NRP research workshop at Norwich Research Park in Jan 2019. |
| Start Year | 2018 |
| Description | China-UK Plant Phenomics Centre |
| Organisation | University of East Anglia |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Coordinated the MoU for jointly establishing a China-UK Plant Phenomics Centre (CUPPC) between NANJING AGRICULTURAL UNIVERSITY (China), HUAZHONG AGRICULTURAL UNIVERSITY (China), Aberystwyth UNIVERSITY (IBERS, UK), Earlham institute (BBSRC UK), Rothamsted Research (Plant Sciences Dept., BBSRC UK), University of East Anglia (UK), and University of Nottingham (UK). |
| Collaborator Contribution | Signed MoU as well as initiated research visits between the seven research organisations. |
| Impact | The establishment of the China-UK Plant Phenomics Centre (CUPPC) in Nanjing and Wuhan. The MoU signed between seven leading research organisations in China and the UK. The detailed research agreement is being signed between NAU and UEA/EI. The first NAU-NRP research workshop at Norwich Research Park in Jan 2019. |
| Start Year | 2018 |
| Description | China-UK Plant Phenomics Centre |
| Organisation | University of Nottingham |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Coordinated the MoU for jointly establishing a China-UK Plant Phenomics Centre (CUPPC) between NANJING AGRICULTURAL UNIVERSITY (China), HUAZHONG AGRICULTURAL UNIVERSITY (China), Aberystwyth UNIVERSITY (IBERS, UK), Earlham institute (BBSRC UK), Rothamsted Research (Plant Sciences Dept., BBSRC UK), University of East Anglia (UK), and University of Nottingham (UK). |
| Collaborator Contribution | Signed MoU as well as initiated research visits between the seven research organisations. |
| Impact | The establishment of the China-UK Plant Phenomics Centre (CUPPC) in Nanjing and Wuhan. The MoU signed between seven leading research organisations in China and the UK. The detailed research agreement is being signed between NAU and UEA/EI. The first NAU-NRP research workshop at Norwich Research Park in Jan 2019. |
| Start Year | 2018 |
| Description | Developing CropQuant Phenotyping Robot and Intel's AI Technologies to Gain Insights of Wheat GxE Interactions in Changeable Environmental Conditions |
| Organisation | Intel Corporation |
| Department | Intel Corporation (UK) Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Led and was selected for CASE PhD studentship - Developing CropQuant Phenotyping Robot and AI Technologies to Gain Insights of Wheat GxE Interactions in Changeable Environmental Conditions (ZHOUE19DTP). |
| Collaborator Contribution | Standard CASE industrial cash and in-kind contribution. |
| Impact | No yet. |
| Start Year | 2019 |
| Description | Syngenta Seeds Case Collaboration |
| Organisation | Syngenta International AG |
| Department | Syngenta Seeds |
| Country | Switzerland |
| Sector | Private |
| PI Contribution | Led the application of artificial intelligence and deep learning in Image-based crop phenomics for predicting seed quality with EI and Syngenta. Was awarded the grant (BB/S507441/1) for a case PhD studentship between Oct/2018 and Sep/2022. |
| Collaborator Contribution | Standard CASE PhD studentship cash and in-kind contribution. |
| Impact | A manuscript is being prepared between EI, JIC and Syngenta for machine-learning based software solution, SeedGerm. |
| Start Year | 2018 |
| Title | CropQuant - data processing of crop images |
| Description | Field of the invention: the present invention relates to data processing of images of a crop, in particular a cereal crop such as wheat, maize or rice, for use in image-based field phenotyping. The method comprises retrieving a series of images of a crop captured over a period of time and identifying, in an image (or "initial image") selected from the series of images to be used as a reference image, a reference system against which other images can be compared, the reference system including an extent of a crop plot and/or one or more reference points. The method also comprises, for each of at least one other image in the series of images, calibrating or adjusting the image using the reference system, and determining a height of a canopy of the crop in the image, a main orientation of the crop and/or a value indicative of vegetative greenness (for example, a normalised green value in an RGB colour space and/or excessive greenness). This can afford greater flexibility when monitoring a crop, particularly large numbers of crops, over periods of months. For example, the method can be used to process images of a crop which have been captured in the field and, thus, subject to vagaries of weather. Moreover, the method can be used for each crop and, thus, allow large data to be processed for large numbers of crops. |
| IP Reference | GB1709756.9 |
| Protection | Patent application published |
| Year Protection Granted | 2018 |
| Licensed | Commercial In Confidence |
| Impact | CropQuant technology has been reported by UK national and EU media over a dozen of times since 2016. It has been featured as a stand-out example of UK-based Agri-Tech innovations and was presented BBSRC's Harvest 2050 and other industry-related activities. Recently, our bespoke farming robot CropQuant Sheila was invited to exhibit at REAP 2017, an event organised by Agri-Tech East, KTN and Innovate UK. |
| Title | CropQuant to measure cereal growth |
| Description | A method of processing (batch processing) images of a crop (pot, plot or field) comprising: retrieving a series of images of a crop (pot, plot or field) captured over a period of time; identifying, in an (initial) image selected from the series of images to be used as (a) reference image, a reference system against which other images can be compared, the reference system including an extent of a crop plot and a set of (key reference points, such as the plot region, the canopy space and) height markers ; and for each of at least one other image in the series of crop growth images. |
| IP Reference | |
| Protection | Copyrighted (e.g. software) |
| Year Protection Granted | 2017 |
| Licensed | Commercial In Confidence |
| Impact | The hardware and software solutions developed by my lab, i.e. the IoT-based crop monitoring solution called CropQuant (UKIPO, GB1709756.9, international PCT Patent Application No. PCT/GB2018/050985) and derived AI-based Agri-Robot (CropQuant-R) have attracted much industrial interest. The East Asia licence is in the process of being sold to a research organisation in China for US$80,000 plus VAT, plus 5% royalty. |
| Title | AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning |
| Description | Aerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. Here, we report AirSurf-Lettuce, an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | Utilising ultra-large normalised difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers' precise harvest strategies and marketability estimates before the harvest. |
| URL | https://www.biorxiv.org/content/10.1101/527184v1 |
| Title | CropQuant Software System |
| Description | The CropQuant in-field phenotyping platform provides a cost-effective Internet of Things (IoT) powered crop monitoring system for wheat and other cereal crops, designed to be easily used and widely deployed in any environment. To manage and process data generated by the platform, we developed an automatic control system, high-throughput trait analysis algorithms, and machine-learning based modelling to explore the dynamics between genotypes, phenotypes and environment. This technology can be applied to breeding, cultivation, crop research, and digital agriculture. |
| Type Of Technology | New/Improved Technique/Technology |
| Year Produced | 2017 |
| Impact | Since September 2017, the preprint of the CropQuant manuscript has been tweeted for 35 times, downloaded for more than 1272 times. CropQuant was reported by BBSRC and other industry-related activities such as Syngenta and Bayer Internal Seminar Series. CropQuant has also been featured as a stand-out example of UK-based Agri-Tech innovations. Recently, our bespoke farming robot using the CropQuant software system, Project Sheila, was invited to exhibit at REAP 2017, an event organised by Agri-Tech East, KTN and Innovate UK. |
| URL | https://github.com/Crop-Phenomics-Group/CropQuant/releases |
| Title | CropSight - a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management |
| Description | CropSight is a PHP and SQL based server platform, which provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralised data management. Data transfer and annotation are accomplished automatically though an HTTP accessible RESTful API installed on both device-side and server-side of the CropSight system, which synchronise daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance whilst different experiments are being conducted. |
| Type Of Technology | Webtool/Application |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | As a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future. |
| URL | https://academic.oup.com/gigascience/advance-article/doi/10.1093/gigascience/giz009/5304887 |
| Title | Leaf-GP Software Distribution |
| Description | Leaf-GP software is published with the software paper entitled "Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat" in Plant Methods in last December. The software is distributed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The software is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. It presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | The Leaf-GP paper generated 18169 downloads within a month and received an attention score of 21 (https://biomedcentral.altmetric.com/details/30811558#score), No.1 amongst all Plant Methods papers (48 in total) published from October 2017 and the attention scoring of the paper is higher than 91% of its contemporaries published from October 2017, worldwide. |
| URL | https://github.com/Crop-Phenomics-Group/Leaf-GP/releases |
| Description | AI in life sciences - Presentation at the Royal Institute of Great Britain, organised by Royal Society of Biology |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Artificial Intelligence - Can AI save the world? Dr Ji Zhou, Phenomics Project Leader at the Earlham Institute. Discussions regarding the inclusion of self-learning algorithms into systems that underpin modern life could help us address many of the global challenges society faces. But they also carry risks that may have devastating results. Can AI truly save the world, or is it too risky to depend on machine learning to solve our problems? This event is presented in partnership with the Royal Society of Biology, the Biochemical Society and the British Pharmacological Society for Biology Week 2018. Biology Week 2018 is from 6th-14th October and showcases the important and amazing world of the biosciences, getting everyone from children to professional biologists involved in fun and interesting life science activities. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.rsb.org.uk/get-involved/biologyweek/royal-institution-debate |
| Description | Automation and Robotics Event Organised by KTN, BBSRC and Innovate UK |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | I gave a talk entitled "An integrated approach to understand our crops, from sky to field", at the KTN Agrifood Automation & Robotics Event in Peterborough on Friday 4th July. Robotic based material handling and processing systems are presented to Agri-Food industry in order to give a major uplift in productivity for food manufacturers. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.oalgroup.com/events/ktn-agrifood-automation-robotics-event-belfast-axleb |
| Description | BBSRC Harvest 2050 - Science Media Centre |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Interviewed by the media in terms of the Zhou Lab's Agri-Tech innovative products, which were then reported by Guardian and other national media. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.theguardian.com/environment/2017/nov/14/miniature-robots-could-cut-pesticide-use-on-farm... |
| Description | Conference organiser of PAG San Diego attracting 3800 plant and animal researchers |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | I am on the organising committee of the largest plant and animal ag genomics conference attracting some 3800 researchers, policy makers, industry etc |
| Year(s) Of Engagement Activity | Pre-2006,2006,2007,2008, |
| Description | DFW Presentation to DEFRA chief science advisor |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | A discussion of the outcomes and impact of WISP and DFW programmes |
| Year(s) Of Engagement Activity | 2019 |
| Description | Exhibit in Royal Norfolk Show |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | My lab exhibited technologies invented by the Zhou laboratory at Norwich Research Park and was interviewed by Royal Norfolk Show, Agri-Tech East, and EDP. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://royalnorfolkshow.rnaa.org.uk/ |
| Description | Interviewed by ITV national news |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | ITV news interviewed my lab members and I to promote our work for robotics in agriculture. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.itv.com/news/anglia/2019-02-08/uea-and-cambridge-universities-at-the-forefront-of-roboti... |
| Description | John Innes Centre open day to the public |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | To show the general public plant science research in practise. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Member of the board of the G20 wheat initative |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | I am a board member of the G20 wheat initiative set up by the G20 agricultural ministers to facilitate coordination of wheat research in the G20 countries. We organise working group to facilitate such coordination, and to identify priorities for funding by funding agencies within the G20 countries |
| Year(s) Of Engagement Activity | 2011,2012,2013,2014,2015 |
| Description | Member of the management board of CGIAR wheat programme (CIMMYT and ICARD) to breed wheat for the resource-poor in the developing World |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | A management board member of CGIAR wheat programme (CIMMYT and ICARD) to breed wheat for the resource-poor in the developing World |
| Year(s) Of Engagement Activity | 2011,2012,2013,2014,2015 |
| Description | NRP Translational Fund public engagement night |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Supporters |
| Results and Impact | Over 200 people attended the NRP translational funding night aimed for raising further funding to support Agri-Tech innovations. I presented CropQuant technologies derived from NRP Translational Fund in the event. |
| Year(s) Of Engagement Activity | 2019 |
| URL | http://www.nrp.ac.uk/nrp-funded-projects/ |
| Description | Norfolk forum Science festival |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | To provide the general public with an insight into plant science research |
| Year(s) Of Engagement Activity | 2017 |
| Description | Organisation a day workshop at Eucarpia meeting , Clermont_Ferrand Paris, with INRA (French) and Proweizen (German ) and CIMMYT researchers |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The worship was to discuss possible collaborations which could lead to bids into the EU for funding |
| Year(s) Of Engagement Activity | 2018 |
| Description | Present AirSurf in REAP 2018 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Showcase our multi-scale plant phenotyping technologies with a focus on the AirSurf platform in the REAP 2018 conference. Over 200 professionals from Agri-Food, Agri-Tech sectors attended the meeting. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.agritech-east.co.uk/event/reap-conference-2018/ |
| Description | Presentation on DFW at the DEFRA stakeholders WGIN meeting at RRES |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Request for more information on the programme |
| Year(s) Of Engagement Activity | 2017 |
| Description | Presentation on WISP/DFW to ACC1 Cereals and Grains conference |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | The presentation was to inform the cereal grain (rice, maize and wheat) processes community on the step change which has occurred in wheat research and the involvement of WISP/DFW in the step change |
| Year(s) Of Engagement Activity | 2018 |
| Description | Presentation on WISP/DFW to the BBSRC legume community |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | The presentation was to describe how the UK coordinated wheat programme was put together, and the impact the programme has had on the step change which has occurred in wheat research |
| Year(s) Of Engagement Activity | 2018 |
| Description | Presentation on WISP/DFW to the BBSRC rice community |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Presentation described how the coordinated WISP/DFW UK wheat programme was put together, and the impact the programme has had on the step change which has occurred in wheat research |
| Year(s) Of Engagement Activity | 2018 |
| Description | REAP 2017 - CropQuant Robot |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | At REAP 2017 we presented our newly invented CropQuant farming robot as an emerging agri-tech innovation for the UK's Agri-Food sector. We also shared our experiences and interacted with business and industrial practitioners in terms of crop monitoring, robotics, machine learning, future visions, and how the CropQuant technology could provide insights into emerging agri-tech developments. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.agritech-east.co.uk/cropquant-grows-with-crop-to-provide-robot-eye-on-performance/ |