Final System Presentation (25 minutes)

Students present and demonstrate their completed AI crop monitoring systems. Each group runs their final program on the mat (about 5 minutes per group) while explaining how their system works using the AI framework: Input → Processing → Output → Action.

Using precise language, students should describe:

• What the system detected using the camera
• What input they gave the AI (prompt or instructions)
• What output the AI generated
• How the program used that output to control Ari
• How decisions were made in their program

After presenting, each group shares one challenge they encountered and how they overcame it.

Launch Data Analysis & Capstone Work (15 minutes)

Rubric

Ask students to turn to the <a href="https://docs.google.com/document/d/12aAWcwtVmoLSC7ooUr0MurZnGRN19d1SxmHtEtQz4/edit?usp=sharing">AI System Capstone Rubric in their Field Notebooks. Explain that the rubric will be used to evaluate both the Data Analysis activity and the AI System Capstone project. Review each criterion and discuss how successful work includes accurately summarizing data, identifying patterns or trends, explaining the roles of humans and AI systems, and communicating findings clearly.

Part A: Data Analysis

Have students turn to Module 2, Lesson 6 in their Field Notebooks and review the <a href="https://docs.google.com/document/d/1EqCSFeH9ibkYSbl2sHbpx8eZtH2IsgNnO5pZv66WGo/edit?usp=sharing">AI System Data Analysis_ page. Explain that students will analyze the data collected while testing their AI system and use their findings to support their capstone project.

Each student submits a short data analysis that includes:

1. Data Collection - A simple data chart that organizes the outcomes of their tests.
2. Summary - A short written summary explaining:

• How the AI system addressed the original problem.
• The Input → Processing → Output → Action of the AI system.
• Patterns or trends they observed in the results.
• The role of the human compared to the role of the AI system.

Part B: AI System Capstone

Students use what they learned from collecting sensor data and testing their AI system to imagine how the technology could be improved or used to solve a new problem. Students may complete one of the following extension projects.

Option 1: Crop Health Report

Use the data collected by your AI system to create a Crop Health Report to guide real farming decisions. Students will act as an Agronomist or Agricultural Data Analyst, using AI-generated data to evaluate field conditions and recommend actions.

Students may present their findings in one of the following formats:

• Slide deck presentation
• Written crop health report structured with headings and data
• Visual poster with their data, claim, and recommendations
• Verbal briefing by presenting findings and recommendations to the class

Report Requirements:

1. Data Summary - Create a simple chart to display the data collected. Number of healthy (green), dehydrated (blue), and dead (red) crops. Organize by location (e.g., left side vs. right side of the field).
2. Collect Sensor Data - Use Ari’s sensors to collect directional and distance data that will help the farmer better understand field conditions (just as you did in lesson 2). Connect this sensor data to the crop health data collected by the AI system by identifying where unhealthy crops are located, measuring the size or distance of affected areas, and describing the direction or location of those areas within the field.
3. Pattern or Trend - Identify something you notice in the data. Example: “Most of the dehydrated crops are on one side of the field.”
4. Claim (What is happening?) - Make a claim about the field based on your data. Example: “This side of the field may not be getting enough water.”
5. Recommendation (What should the farmer do?) - Suggest actions the farmer should take based on your claim. Example: “The farmer should increase irrigation in this area.”
6. Evidence-Based Explanation - Explain how your AI system and data support your claim and recommendation. What the system detected? What data was collected? What evidence from your data supports your claim? Why your recommendation would improve the field

Option 2: Smart Farming System Design

Design a specific AI-powered machine or system that helps farmers monitor or care for crops. Students will act as an Agricultural Engineer or AgTech Designer, creating a tool that could be used on a real farm.

Students should pick a specific type of technology learned in lesson 1, such as:

• AI Farming Drones
• Autonomous Tractor
• AI Farming Robots
• Smart Irrigation & Monitoring Systems

Design Requirements:

1. Problem + Purpose - What does your system do? What job is your machine responsible for? Example: “This drone scans crops and waters only the ones that are dehydrated.”
2. Input - What does the AI detect? What data is collected? Example: crop color, location, plant condition
3. Output → Action - How does the system respond? What does the AI output? What does the machine do with that information? Examples: Detects wilting → waters that area. Detects dead crop → flags or removes it. Detects healthy crop → moves on.
4. How does this help the farmer? - Saves time? Uses less water? Improves crop growth?
5. Draw Your Design - Sketch your system in action on a farm. Label: Where the AI is used, what it detects, and what actions it takes.

Option 3: Improved Code Design

Extend your AI system by adding the final human decision-making step into the program. Students will act as an AI Systems Engineer, designing how AI systems and humans work together to make decisions.

Design Requirements:

1. Create the Human Decision Prompt - Students have created an AI system that collected data on the health of each crop. Now, they will ask the farmer what to do next. This will appear on Ari’s screen.
2. Ask the Question - In the Editor, add a cancelable prompt for string block to display a message. Example: “This plant is dehydrated. Should I water it?”
3. Add Response Options - This is where we add selections for user input. The human acts as the final decision-maker, choosing what step to take next. Click on the gear icon to add two response options. Then add text blocks to add selections “Yes” and “No.”
4. Store the Response - Once the farmer makes a selection, we can save the answer in a variable (ex: decision) so the program can use it.
5. Add Conditional Logic - Next create an if/else condition to control Ari’s behavior. If decision = “Yes” → Ari performs an action (water, pause, signal). If decision = “No” → Ari continues moving or logs data.
6. Apply to Each Crop Condition - Students will repeat this structure for each crop type. They can decide what actions would be taken if the plant is healthy, dehydrated, or dead.
7. Test with the Farmer - Students will find a classmate to act as the farmer, making the selections on either Ari’s screen or the terminal. Make sure the system responds correctly based on user input.

Independent Data Analysis & Capstone Development (35 minutes)

Students work at their own pace to complete the following:

Part A: AI System Data Analysis

Part B: AI System Capstone

Encourage students to reference the rubric throughout their work and use evidence from their collected data to support their explanations and decisions. Students should submit both their completed AI System Data Analysis page and their finished capstone project for assessment.