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• Inactive for Year: 2026/27

• Module Leader(s): Dr Ankush Prashar

• Owning School: Natural and Environmental Sciences
• Teaching Location: Â鶹´«Ã½ City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Aims

This module in Agritechnology equips students with foundational and practical skills to harness digital technologies for sustainable crop production and environmental management. Module aims introduce core concepts in precision agriculture (PA), remote sensing principles, and their integration across crop, soil, and biodiversity applications, while building proficiency in decision support systems (DSS), mapping tools, sensor platforms, and software workflows.

Students develop foundational skills in remote sensing fundamentals, soil measurement techniques, digital data fundamentals (Internet of Things (IoT), machine learning (ML)), and PA platforms, establishing a theoretical framework linking geospatial data to agronomic decision-making.

Practical skills are fostered through hands-on computer cluster sessions analysing real PA datasets, sensor deployment exercises, and software applications for generating actionable maps and DSS outputs.

The module cultivates students' ability to translate multi-source data (spectral, IoT, environmental) into farm-ready insights, bridging domain knowledge with computational thinking. As a data science core degree, it positions Agritechnology as a critical application domain, demonstrating how scalable analytics, machine learning pipelines, and sensor fusion solve real-world challenges in yield optimisation, resource efficiency, and biodiversity monitoring, skills directly transferable to any agribusiness, policy, and research

Outline Of Syllabus

• Introduction to Precision Agriculture & Remote Sensing
• Crop, Biodiversity & Soil Sensing Applications: Vegetation indices, phenology,
stress monitoring, proximal/soil tools.
• Platforms, Mapping & Decision Support: Drone/mobile/satellite systems,
GIS (Geographic Information System) software, variable rate tech, DSS design.
• Digital Data & IoT/ML Foundations.
• ML/IoT Applications: Predictive analytics, real-world case studies.
• Hands-on Sensors & Integration Project: Field exercises and data fusion.
• Computer Cluster Practical’s: Data processing.
• Examples from Industry use

Teaching Methods

Teaching Activities

Teaching Rationale And Relationship

This module delivers core content through integrated computational workshops that combine short lecture segments with hands-on exercises. This blended approach ensures students immediately apply theoretical concepts such as data standards, programmatic access, and legal considerations within practical contexts, directly supporting the intended knowledge outcomes.

Computational workshops guide students through retrieving, cleaning, and integrating real-world biological and environmental datasets, aligning closely with all three intended skills outcomes. Weekly portfolio tasks reinforce learning by providing structured, incremental challenges that build technical proficiency and promote critical thinking.

To enhance real-world relevance and career insight, asynchronous industrial talks are embedded throughout the module. These recorded contributions from professionals in biotechnology and environmental sectors offer students exposure to current data challenges, tools, and applications in industry. They complement the technical content by contextualising it within real life decision making and innovation processes.

The final project-based assessment consolidates learning by requiring students to design, document, and justify a complete data strategy for an industrially relevant bioscience problem. This applied, practice led teaching model ensures students develop both conceptual understanding and technical capability, preparing them for data centric roles in research and industrial biotechnology

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams

Other Assessment

Assessment Rationale And Relationship

This dual-assessment strategy (50% presentation, 50% research portfolio) balances knowledge synthesis with practical application, ensuring students master both communication and technical workflow design critical for agritech careers. The presentation ("High throughput approaches for precision farming on novel crops") evaluates students' ability to research, critically analyse, and communicate cutting-edge PA applications, building skills in literature review, trend evaluation, and stakeholder presentation, essential for pitching innovations to farmers, industry, or funders.

The research portfolio (PA analysis workflow) complements this by requiring students to translate theoretical understanding into operational workflows or pipelines from raw sensor data to decision making. Together, they create a holistic evaluation: presentation tests 'what' (current science, novel applications) while portfolio tests 'how' (implementable workflows), mirroring real-world roles where agronomists must both understand emerging tech and deliver farm-ready decisions.

Synergy: Presentation insights (e.g. novel crop phenotyping methods) directly inform portfolio workflow designs, ensuring theoretical knowledge drives practical solutions. Both emphasise data science application, translating multi-source PA data into actionable agronomic decisions, while group formats build collaborative problem-solving for industry teamwork. This combination guarantees MSc graduates can both speak the language of innovation and build the portfolio to deliver it.

Reading Lists

Timetable

• Timetable Website: