Course in Artificial Intelligence in Agricultural Planning
Online course of 300 h in artificial intelligence in agricultural planning. 6 modules with Regulation (EU) 2024/1689 (Artificial Intelligence Act), ISO/IEC 42001 (AI management systems) and Python (scikit-learn, XGBoost). Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify the farming decisions that can be supported by data.
Prepare and clean field, climate and satellite data.
Train yield and phenology prediction models.
Apply optimisation techniques to the planning of field operations.
Convert model outputs into prescriptions that can be executed in the field.
Evaluate ethics, data ownership and the requirements of the AI Act.
Develop an applied AI case for farm planning.
Who it's for
- Agricultural data scientist looking to specialise in artificial intelligence in agricultural planning.
- Digital agriculture engineer looking to specialise in artificial intelligence in agricultural planning.
- Agricultural planning adviser looking to specialise in artificial intelligence in agricultural planning.
- Remote sensing analyst looking to specialise in artificial intelligence in agricultural planning.
- Product owner of agtech solutions looking to specialise in artificial intelligence in agricultural planning.
- Digital agricultural transformation consultant looking to specialise in artificial intelligence in agricultural planning.
- Graduates in engineering or science seeking to enter precision agriculture.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/IEC 42001 (AI management systems)
- Regulation (EU) 2016/679 (GDPR)
- EU Code of Conduct on agricultural data sharing
- ISO 11783-10 (ISOBUS, task controller)
- ISO/IEC 23894 (AI risk management)
- Regulation (EU) 2023/2854 (Data Act)
6 modules · 300 h
1.1Campaign decisions: what, when, how much and where1.2Plot data: soil, history and topography1.3Weather data and forecasts1.4Remote sensing and vegetation index series1.5Machinery data and records of operations carried out1.6Market and price data1.7Data quality and common gaps1.8What artificial intelligence can and cannot contribute1.9Risk of deciding with poorly validated models1.10Workshop: data and decision inventory for a farm
2.1Delineation of plots and management zones2.2Spatial and temporal alignment of sources2.3Building series by plot and by season2.4Derived agronomic variables2.5Handling missing and outlier values2.6Data from few seasons: limitations2.7Enrichment with public data2.8Storage and reuse of variables2.9Documentation of the dataset2.10Practice: building a plot database
3.1Yield prediction by zone3.2Irrigation requirement models3.3Pest and disease risk prediction3.4Optimal timing models for field operations3.5Crop growth models and their combination with data3.6Validation with seasons not used in fitting3.7Prediction uncertainty and its use3.8Model interpretability3.9Comparison with the farmer's judgement3.10Practice: yield prediction model
4.1Rotation planning with multiple criteria4.2Crop allocation to plots4.3Irrigation optimisation with limited resources4.4Operations scheduling with machinery constraints4.5Harvest sequencing4.6Optimisation under price and climate uncertainty4.7Decision models and scenario analysis4.8Trade-off between yield, cost and risk4.9Available optimisation tools4.10Practice: optimised season plan
5.1From prediction to zone-based prescription5.2Variable-rate maps and their format5.3Integration with the machine terminal5.4Field verification of the prescription5.5Feeding execution data back into the model5.6Automatic season documentation5.7Interoperability between platforms5.8Limited connectivity and fallback modes5.9Team training in the use of the tools5.10Practice: generating and loading a prescription
6.1Ownership of the farmer's agronomic data6.2Transfer to platforms and suppliers6.3Technological dependency and exit plan6.4Transparency of automated recommendations6.5Liability for an erroneous recommendation6.6Sustainability and input reduction6.7Season outcome indicators6.8Common mistakes in digital agriculture projects6.9Adoption roadmap for a farm6.10Final project: decision support system for a season
How you'll learn
Training 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn, XGBoost), Google Earth Engine, QGIS, R, PuLP / OR-Tools, in free, academic or evaluation versions. The case studies draw on real procedures for yield prediction models, scheduling of field operations and generation of variable-rate prescriptions. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify the farming decisions that can be supported by data.
Prepare and clean field, climate and satellite data.
Train yield and phenology prediction models.
Apply optimisation techniques to the planning of field operations.
Convert model outputs into prescriptions that can be executed in the field.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical regulatory reading.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 300 hours spread over 3 to 5 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
Work is carried out with Python (scikit-learn, XGBoost), Google Earth Engine, QGIS, R, PuLP / OR-Tools. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An SEIUM Institute diploma on passing the continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the advanced programme. Check the recognition policy when you enrol.
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