A PhD position in Ulster University: Artificial Intelligence in Farms: AI-based Crop Disease Monitoring and Detection


Plant diseases may affect the root, steam and leaves of plants resulting in a sizable drop of revenue for farmers as crop’s quality is affected and may lead to food shortage and food chain disruption [1]. Traditionally, a crop disease can be detected by visual inspection which can be a tedious enterprise which is time and effort consuming, and errors prone. Farming has developed extensively in the last few decades taking advantages from developments in chemistry, physics, sensing technology, data processing and analytics, artificial intelligence and IoT [1,3-4]. The demand for mobile portable applications in agriculture has increased as portable technology ubiquitousness allows for a wider deployment and a better cost-effectiveness. With the technology, farmers can identify and detect early infections and diseases and hence mitigate their impact, improve treatments outcome and can prevent further infections from re-occurring. Portable spectroscopy can be used to detect the presence of diseases on leaves and categorise healthy plants from unhealthy ones. Such a technology has found use in many agro-food applications as it offers short processing times, cost-effectiveness, portability and ease-of-deployment [2,5].

Spectroscopy is the analysis of matter and its interaction with electromagnetic radiations; and a spectral signature is the variation of reflectance or emittance of a material with respect to wavelengths. It is a non-destructive way to find the fingerprints of components; and hence is a suitable method to inspect plants’ samples.

Reflectance is a measure of electromagnetic energy that bounces back from the surface of a material; and the leaf reflectance in the visible and near-infrared ranges are influenced by a variety of interactions (including leaf surface and water content) which can lead to a suitable use in classification and detection. Further, green vegetation spectral signatures can show pigmentation in plant tissues as Chlorophyll growth is affected. Hence it can be used for anomaly detection in remote sensing applications. Counting the number of insects of various species is important for planning pest control, and for guiding agricultural policy. Computer vision algorithms can be trained with the captured footage to detect the soil conditions, analyse the aerial view of the overall agricultural land, and assess crop health information. Computer vision-enabled machines can be used in sorting and grading the harvest; while automating such tasks can offer efficiency [2,3].

Hyperspectral imaging in agriculture can significantly extend the range of farming issues that can be addressed using remote sensing. Almost every farming issue (weeds, diseases, etc.) changes the physiology of plants, and therefore affects its reflective properties. Healthy and unhealthy crops reflect the sun light differently which renders it possible to detect such changes in the physiology of the plants and correlate them with spectra of reflected light.

Hence the objectives of this research proposal are:

To address the complexity of crop disease monitoring and detection in the context of smart farming taking account of different data types.

To develop a solution that integrates both computer vision and spectroscopy related information.

To design an AI based system for classification of diseases and anomaly detections.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed

for further information, please visit https://www.ulster.ac.uk/doctoralcollege/find-a-phd/1455586

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