Dr Kanthu Joseph Mhango

Supervisor Details

Contact Details

Agriculture and Environment, Harper Adams University

Scientific Background

Research Interests

Dr Kanthu Joseph Mhango is a Senior Lecturer in Applied Data Science at Harper Adams University, where he combines academic research with extensive industry experience in building decision support systems for agriculture. His expertise spans agronomy and crop physiology, process-based crop growth modelling, machine learning, and the development of cutting-edge physics-informed neural networks for crop productivity forecasting.
His broader research interests include improving photosynthetic efficiency, high-throughput plant phenotyping, and food security research. These interests reflect a philosophy grounded on using data-driven methods to create decision support systems that help manage crops and ecosystems more efficiently.

Research Groups

Centre for Agricultural Data Science (AgriDat)

Supervision Style

In three words or phrases: Supportive, outcome-oriented, proactive

Provision of Training

I prefer to oversee and take responsibility of your initial training, but with a clear pre-agreed path to independence that we regularly appraise together. I will closely follow and supervise your training, helping you to build demonstrable independence overtime.

Progression Monitoring and Management

I will expect reasonably close contact with you to maintain a unified position of your progression throughout your research. We will maintain a collaborative management system to keep track of milestones and take remedial actions where progress is slow.

Communication

I have an open door policy, and value setting up a collegial environment. I will expect you to manage your work/life balance.

Meetings

PhD Students can expect scheduled meetings with me at least once per week. These meetings will be a mixture of face-to-face and via video chat or telephone. I am usually contactable for an instant response on every working day.

Work Patterns

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings) I expect students to manage their own time.

Notice Period for Feedback

I need at least 1 week’s notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Synchronising Potato Tuber Initiation with Transient Hormone Control to Improve Size Uniformity

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Synchronising Potato Tuber Initiation with Transient Hormone Control to Improve Size Uniformity

Secondary Supervisor(s): Dr Alexander Watson-Lazowski, Dr Joe Roberts

University of Registration: Harper Adams University

BBSRC Research Themes:

Sustainable Agriculture and Food (Plant and Crop Science)
Understanding the Rules of Life (Plant Science, Systems Biology)

Project Outline

Uniform tuber size in a harvest adds value in potato supply chains. The size distribution is partially determined by the hierarchy with which stolon tips switch into tubers during growth. If many make this switch in a short window, the resulting tuber cohort grows more evenly. This project tests a two-step approach to encourage this synchrony without yield sacrifice.

Step 1 is a reversible treatment with a transient strigolactone (SL) inhibitor to encourage simultaneous bud activity at stolon tips without lasting side effects. Step 2 applies a gibberellin (GA) regulator timed after the SL inhibitor's activity tapers off, to boost tuber size by skewing carbohydrate partitioning to the tubers.

The project will: (1) find the minimum effective SL inhibitor dose and timing; (2) test whether later GA moderation preserves or improves marketable yield; and (3) build predictive models for tuber initiation timing from early growth data.

Operating across rhizotrons, glasshouse and polytunnel trials, the researcher will leverage computer vision to record tuber formation non-destructively, gene expression tests (RT-qPCR) to track StSP6A (a tuberigen) and StGA2ox1 (a marker of GA activity), while targeted chemical assays will quantify SL and GA activity. Canopy photogrammetry and spectroscopy will quantify phenology and photosynthesis.

Data from all phases will be used to test the hypotheses, but also feed a causal machine learning model, to predict days to peak tuber formation from crop phenology. The outcome will be practical timing models and an evidence-base for manipulating apical dominance to manage synchrony in tuber initiation and growth. The researcher will be trained into a multifaceted crop physiologist with strong analytical skills, data science, and crop modelling competencies, with digital phenotyping and molecular analytics experience.