PhD Study : 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

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

  • Full Award: (full-time tuition fees + £19,000 (tbc))
  • Part Award: (full-time tuition fees + £9,500)
  • Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

Recommended reading

N.N. Che’Ya,, N.A. Mohidem, ; Roslin, N.A.; Saberioon, M.; Tarmidi, M.Z.; Arif Shah, J.; Fazlil Ilahi, W.F.; Man, N. Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review. Agronomy 2022, 12, 967.

F. Asharindavida, O. Nibouche, J. Uhomoibhi, H. Wang, and J. Vincent, “Evaluation of olive oil quality using a miniature spectrometer: A machine learning approach,” in Proc. SPIE, vol. 11754, pp. 17–28, Apr. 2021.

P. A. Dias, A. Tabb, and H. Medeiros, “Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3003–3010, 2018.

Daniel Caballero, Rosalba Calvini, José Manuel Amigo,Hyperspectral imaging in crop fields: precision agriculture, Data Handling in Science and Technology, Elsevier, vol.32,2019, pp.453-473.

M. Ahmad, Muhammad, Asad Khan, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Ahmed Sohaib, and O. Nibouche. 2019. "Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images" Remote Sensing 11, no. 9: 1136

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 27 February 2023

Interview Date
week commencing 17 April 2023

Preferred student start date
18 Sept 2023


Apply Online  

Contact supervisor

Dr Omar Nibouche

Other supervisors