FarmBeats for Students
Today's farms are beginning to look a lot more like smart cities. Growers are using modern techniques like sensors, computer vision, and artificial intelligence to acquire a more complete view of their crops. These methods help them make better decisions, discover inefficiencies, and unlock new insights into improving food production. The FarmBeats for Students program brings these modern tools into the hands of today's learners.
The program combines an affordable hardware kit with curriculum and activities designed to give students hands-on experience in applying precision agriculture techniques to food production. Using an array of sensors, students stream and analyze data in Excel. With Lobe.ai, they build, train, and apply machine learning models to track and inform plant health. Activities where have students setup an agent and work with a curated big data set. The learning progression enables students to easily see the connections between these modern agriculture tools and the opportunities they afford.
Engaging student experiences
Using FarmBeats for Students, students learn about AI, Machine Learning, and the Internet of Things (IoT) by building a garden monitoring system. They assemble a Raspberry Pi equipped with atmospheric and environmental sensors to understand their soil's health, analyze data, and make decisions. The student-built IoT devices connect to custom Excel workbooks that collect real-time data using Excel’s Data Streamer. Using Lobe.ai, students are introduced to building their own Machine Learning models, applying the technique to predict nutrient deficiencies in their plants, and identifying pests in their garden. The program ends by introducing a responsible AI framework, engaging students with some of the social and ethical challenges raised by this new technology.
The five lesson plans and activities have been designed to align with the AI4K12's AI education guidelines and the 5 Big Ideas.
Comprehensive educator resources
The curriculum concepts and performance objectives are backed by the educational standards that are important to you. The content includes a detailed 20-day timeline, hardware build instructions, student activity guides, sample questions & answers, agriculture and technology teacher notes, and PowerPoint presentations.
Big Idea 1: Perception
Students build their own IoT device with a Raspberry Pi and an array of sensors.
- Introduction to FBFS presentation
- Perception presentation
- Activity 1: Sensors for watering decisions
- Activity 2: Sensors for growing optimization
Big Idea 2: Representation & Reasoning
Students create an AI agent to notify them of soil moisture levels.
- Representation & Reasoning presentation
- Activity 1: Construct an AI agent
- Activity 2: Calculate growing degree units (GDUs)
Big Idea 3: Learning
Students use ML models to identify nutrient deficiencies in plants.
- Learning presentation
- Activity 1: Big Data analysis
- Activity 2: ML models for nutrient deficiencies
Big Idea 4: Natural Interaction
Students use AI to identify pests in their garden with Lobe.
- Natural Interaction presentation
- Activity 1: ML models for garden pests
Big Idea 5: Societal Impact
Students discover how AI impacts them and the world around them.
- Societal Impact presentation
- FBFS Conclusion presentation
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