Using spectral signatures as a toolbox for determining crop health status.
Lead Research Organisation:
University of Essex
Department Name: Biological Sciences
Abstract
The School of Biological Sciences at the University of Essex, invites applications for a four-year, full-time fully funded BBSRC Industrial CASE PhD studentship for the academic year 2015/16. Changes in plant physiology, biochemical and structural properties are influenced by climate/environmental factors, which in turn directly influence the optical properties of the vegetation, providing the basis for the use of spectral reflectance as a tool for remote sensing to assess plant health status. Specific reflectance bandwidths can provide an indication of a variety of indices, such as vegetative biomass, water status and photosynthetic performance; as well as nitrogen and chlorophyll content. These spectral signatures provide a powerful "tool" for remotely identifying and assessing crop health status. These tools have been incorporated in many remote sensing platforms including the recent use of unmanned aircraft or drones to identify key crop parameters.
This research will use a novel approach combining known and new techniques in plant physiology and remote sensing to identify new spectral signature relative to crop status and combine these with existing spectral bands to produce a cutting-edge spectral signature toolbox. It will provide original blue prints for an innovative practical analytical portable field tool for identifying crop performance. The research will create new knowledge and strategies for plant & crop health and detection systems to distinguish abiotic and biotic stressors. The project will suit a person interested in plant/crop biology, physiology, remote sensing and instrumentation development. This project is in collaboration with industrial partner Dr Iain Cameron from Environment System Ltd., an environmental and geographic intelligence consultancy. Additionally time will be spent with a 2nd Industrial partner Callen-Lenz Associates who are specialists in image capture and research including the use of drones. The research will provide training in plant physiology, highlighted by BBSRC as a key skills shortage area.
This research will use a novel approach combining known and new techniques in plant physiology and remote sensing to identify new spectral signature relative to crop status and combine these with existing spectral bands to produce a cutting-edge spectral signature toolbox. It will provide original blue prints for an innovative practical analytical portable field tool for identifying crop performance. The research will create new knowledge and strategies for plant & crop health and detection systems to distinguish abiotic and biotic stressors. The project will suit a person interested in plant/crop biology, physiology, remote sensing and instrumentation development. This project is in collaboration with industrial partner Dr Iain Cameron from Environment System Ltd., an environmental and geographic intelligence consultancy. Additionally time will be spent with a 2nd Industrial partner Callen-Lenz Associates who are specialists in image capture and research including the use of drones. The research will provide training in plant physiology, highlighted by BBSRC as a key skills shortage area.
People |
ORCID iD |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M017125/1 | 01/10/2015 | 30/09/2019 | |||
| 1721987 | Studentship | BB/M017125/1 | 01/10/2015 | 02/10/2019 |
| Description | We investigated the use of optical and imaging based techniques to identify spectral signatures that can be used to assess crop health status, through the development of a theoretical 'toolbox' of spectral techniques and measurements. The ability to measure nitrogen content, crop water status, and environmental factors such as temperature and humidity, which all affect crop health and performance, is essential to improving crop yields. Conventional techniques can be destructive or slow, limiting the ability to measure entire crops across multiple fields. Imaging and optical based techniques, on the other hand, rapidly and non-destructively allow for the continuous measurement of crop health for large areas of crops. A theoretical spectral 'toolbox', consisting of chlorophyll fluorescence, thermography and spectral reflectance, was used to assess plant health, namely water status and chlorophyll content, of sprint wheat. The spectral index of 800nm/550nm (dividing the value of reflectance as measured from an illuminated leaf at the wavelength of 800nm, by reflected light at 550nm), among others, can be used to assess leaf nitrogen content, while the index of 800nm/440nm can assess leaf water status. Chlorophyll fluorescence can be used to indicate overall plant performance and changes in soil RWC, while the chlorophyll fluorescence parameter of Fv/Fm is sensitive to plant nitrogen status. The Normalised Difference Vegetation Index (NDVI) is a common spectral index which uses specific spectral regions to assess crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use imaging systems will increase the uptake of NDVI imaging as a tool for precision agriculture. We described a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets were used to calibrate images and produce robust measurements of NDVI, which is the first time this was described using the Raspberry Pi Camera and can be used with other camera based system. The resulting imagery showed strong performances against 'gold-standard' spectrometry measurements, and a commercial imaging system. |
| Exploitation Route | The |
| Sectors | Agriculture, Food and Drink,Environment |
| Title | Improved reflectance measurement |
| Description | Spectral imaging is a useful tool for high throughput phenotyping, and can be related to a large variety of biological parameters. The Normalised Difference Vegetation Index (NDVI) is a common spectral index which uses specific spectral regions to assess crop health and performance. Increasing the accessibility of spectral imaging systems through the development of small, low cost, and easy to use imaging systems will increase the uptake of NDVI imaging as a tool for precision agriculture. We described a method for using a dual camera system connected to a Raspberry Pi to produce NDVI imagery, referred to as NDVIpi. Spectral reference targets made from commercially available fabric were used to calibrate images and produce robust measurements of NDVI. The resulting imagery showed strong performances against 'gold-standard' spectrometry measurements, and a commercial imaging system. Using the Raspberry Pi allows for users to build and use a robust and accurate NDVI imaging system made from off-the-shelf components. |
| IP Reference | GB1917600.7 |
| Protection | Patent application published |
| Year Protection Granted | 2019 |
| Licensed | No |
| Impact | Currently attempted to commercialise the system in the form of pre-made camera units, using 3D printed cases and components. |
| Title | Methodology to calibrate NDVI imagery |
| Description | We developed a methodology to allow for NDVI imagery to be captured using low cost cameras provided by the Raspberry Pi system. The key component is the calibration of images converting image pixel data into reflectance (%). Once calibrated, NDVI can be calculated and converted back into an image format. Calibration allows for accurate NDVI values that are not dependent on e.g. lighting conditions, camera settings etc, and images collected over time (or with other devices that can measure NDVI such as other cameras or spectrometers), can be compared against any NDVI image produced with this method. This method effectively bring robust NDVI imagery into something that is very affordable in comparison to other commercial systems, without sacrificing accuracy of measurements. |
| Type Of Technology | New/Improved Technique/Technology |
| Year Produced | 2017 |
| Impact | It has helped research within the laboratory, by allowing quick and easy measurements of NDVI - as well as integration into existing systems. This has effectively increased our capacity for plant phenotyping, and depth of data that can be obtained. |
| Description | Physiological Tools for Phenotyping Rice Photosynthesis |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Similar to previous workshop. The aim of the workshop was to exchange plant phenotyping techniques with researchers at Khon Kaen University. The participants consisted of a mixture of academics, plant breeders and rice farmers, who will use this information to improve the yield of rice and other crops through the use of low cost cutting edge phenotyping techniques. We presented the Raspberry Pi NDVI system that we had developed, and both taught the theory behind it, as well as how to practically use the tool. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Two-day workshop on 'Low cost phenotyping tools, for improving rice photosynthesis' with a university in Thailand |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | The aim of the workshop was to exchange plant phenotyping techniques with researchers at Khon Kaen University. The participants consisted of a mixture of academics, plant breeders and rice farmers, who will use this information to improve the yield of rice and other crops through the use of low cost cutting edge phenotyping techniques. We presented the Raspberry Pi NDVI system that we had developed, and both taught the theory behind it, as well as how to practically use the tool. |
| Year(s) Of Engagement Activity | 2019 |