NPL-Cambridge CASE studentship studentship ‘Vegetation assessment using machine learning techniques on spectral imaging data’

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It is estimated that by 2050 70% of more food needs to be produced worldwide. It is therefore not only essential to use our resources as efficiently as possible, but also to assess and mitigate the risk to crops. Farming is increasingly driven by machines and with less staff it is difficult for farmers to monitor their crop. There is the drive to use airborne technologies as well as satellite imagery to do this remotely and automate this. The project aims to build up an extensible, probabilistic framework to do this resulting in a database and data model.

The ultimate aim is to not just have a database of the spectral signatures of different plant species, but also to incorporate phenology and health status of the plants. With regards to crops this will help mitigate the risks of droughts and diseases. Irrigation can be directed where needed and fertilizer used more effectively. Early intervention can stop diseases spreading and there will be less use of pesticides and fungicides. Being able to choose an optimal time to harvest will lead to less food wastage.

Using sophisticated machine learning techniques, sparse models of the land cover can be created. These models will help where there is limited up and downlink bandwidth as there are with airborne technologies as well as with satellites. The device gathering the data can carry a sparse model and only where new data is significantly different to the model action is necessary. In the first instance this action will be an alert to an anomaly. Further analysis is then necessary whether the anomaly is expected due to e.g. change of the season, or the anomaly needs intervention or the model needs updating (e.g. a change of crop).

The aim of this PhD project is a probabilistic model of spectral signatures of plants incorporating phenology and diseases. To this end various machine learning techniques will be employed and benchmarked against each other. Data from the CropScape database ( will be combined with data from Avaris ( for initial analysis. This can then be enriched with data from the Sentinel satellites for temporal analysis, since the revisit times are shorter. Different resolutions and number of spectral bands need to be given consideration. The techniques will also be assessed on their ability to generate knowledge automatically, for example in which way the spectral signature of a plant changes under increasing drought conditions and whether there are underlying general principles. Another aspect is the development of a confidence measure identifying and quantifying classification error.

The successful candidate will be working closely with David Coomes (Cambridge), Anita Faul (Cambridge), Alistair Forbes (NPL), and Carola-Bibiane Schönlieb (Cambridge).

Applicants should have a masters degree in mathematics or a closely related subject, e.g. an engineering degree with a strong mathematical foundation, and should be UK or EU nationals.

The funds for this studentship are available for 3 years in the first instance.

In order to be considered for this studentship please submit a formal application to the PhD in Applied Mathematics and Theoretical Physics, University of Cambridge via the University’s Graduate Admissions website (for more information on this please visit; and send an expression of interest email to which explains why you are interested in this studentship.

Applications should be submitted online until the 31st of March 2017. Expressions of interest letter that briefly describe your motivation for this project should be sent to by the same date.

Please quote reference LE11533 on your application and in any correspondence about this vacancy.

The University values diversity and is committed to equality of opportunity. The Department would particularly welcome applications from women, since women are, and have historically been, underrepresented on our student cohort.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

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