The ‘Predicting Crop Pests Using AI’ course offers you the opportunity to delve into a a booming sector, where the demand for experts in advanced technologies for smart farming is on the rise. In a context where the food security It is crucial to have the ability to predicting and managing pests using artificial intelligence has become essential. This course will equip you with the skills to integrate smart sensors, manage agronomic data y applying machine learning in greenhouses. You’ll learn how to implement agricultural IoT systems, you will develop skills in data collection and analysis, and you’ll find out how optimise automated decision-making for climate control, irrigation and fertilisation.
Cross-cutting
AI Academy
3 pm
The use of AI-powered IoT sensors for smart greenhouse management
AI Academy
3 pm
Introduction
Objectives
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Understanding the use of the IoT in agriculture to improve efficiency.
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Identify key sensors and actuators to optimise greenhouses.
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Implement data collection and management techniques in the cloud.
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Apply artificial intelligence for predict pest outbreaks effectively.
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Design predictive models that optimise the climate and growing conditions.
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Detect malfunctions in agricultural sensors using AI.
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Automate decisions on irrigation and ventilation using smart systems.
Table of Contents
TEACHING UNIT 1: THE INTERNET OF THINGS IN AGRICULTURE
1. The Internet of Things (IoT)
2. General architecture of an IoT system for the agricultural sector
3. IoT communication protocols
4. Security and privacy in agricultural IoT systems
TEACHING UNIT 2: SMART SENSORS AND ACTUATORS FOR GREENHOUSES
1. Classification and selection of sensors for key agronomic variables
2. Sensor integration: temperature, humidity, light levels….
3. Actuators: control of irrigation, ventilation, lighting and nutrients
4. Calibration, maintenance and durability of sensors in harsh environments
5. Real-time data acquisition architecture and edge computing
TEACHING UNIT 3: COLLECTION AND MANAGEMENT OF AGRONOMIC DATA
1. Design of sensor networks and cloud storage
2. Data acquisition and transmission systems: gateways and nodes
3. Data visualisation on IoT dashboards: key metrics for crop production
4. Data pre-processing: standardisation, cleaning and validation
5. Integration with agricultural management platforms
TEACHING UNIT 4: THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN GREENHOUSES
1. Fundamentals of AI and machine learning applied to the agricultural sector
2. Predictive models for indoor climate and growing conditions
3. Detection of anomalies and sensor faults using AI
4. Automated decision-making systems for irrigation, ventilation and fertilisation
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