Unlocking data insights with machine learning and AI

  • 5 minutes

The curriculum series concludes with Section 3, Unlocking Data Insights with Machine Learning and AI. This section is intended to be taught over the duration of eight days. Each day is considered a 45-minute class period. A detailed timeline can be found in FarmBeats for Students Teacher Guide with all activities. Educators should follow the sequential order of instructional days to ensure a cohesive and meaningful learning experience.

Before starting this section, ensure you have these instructional resources and materials:

Teacher documents

  • Teacher Guide - Activity 1.6, Activity 1.7
  • AI4K12 Poster: 5 Big Ideas
  • Activity Set 1 Check for Understanding Key

Student documents

  • Activity 1.6 Color Codes
  • Activity 1.7 Pest Detector
  • FarmBeats - Big Data Workbook

PowerPoint presentations

  • Big Idea 3: Learning
  • Big Idea 4: Natural Interaction
  • Big Idea 5: Societal Impact
  • Conclusion to FarmBeats for Students

Supplies and materials

What is machine learning?

Machine learning is a way of teaching computers to learn and make decisions without being explicitly programmed for every task. Instead of writing detailed instructions, we give the computer data and let it figure out patterns, make predictions, or take actions based on that data.

How does it work?

  • Data input: Imagine teaching a machine to recognize pictures of cats. First, you provide a dataset of cat images (called training data).
  • Learning from data: The machine looks for patterns in the data. For example, cats usually have fur, whiskers, and pointy ears.
  • Model creation: Based on what it learns, the machine creates a model (a representation of what a "cat" looks like).
  • Making predictions: After training, the model can analyze new images and predict whether they show a cat or not.

Explanation of activities

There are two specific activities with Section 3, Unlocking Data Insights with Machine Learning and AI:

  • Activity 1.6: Color Codes
  • Activity 1.7: Pest Detector

These activities are centered around the concepts of machine learning and AI.

Activity 1.6 - Color Codes

In this activity, students create a GenAI Teachable Machine model to predict plant health based on leaf color, an indicator of nutrient deficiency, and train the GenAI Teachable Machine model to improve its accuracy.

GenAI Teachable Machine is a simplified example of guided machine learning (ML). Students provide photographs and information about what the plant's leaves look like when experiencing nutrient deficiency. The GenAI Teachable Machine automatically builds the model and quickly trains it using these images.

Activity 1.7 - Pest Detector

In this activity, students research pest management and use Machine Learning (ML) to identify crop pest species. Students learn how pests, such as animals, insects, and weeds, can significantly impact agricultural productivity. They investigate different strategies farmers use, including pesticides, biological controls, and Integrated Pest Management (IPM)—an approach promoted by the USDA's National Institute of Food and Agriculture. IPM strategically combines monitoring, biological, and chemical methods to sustainably manage pests while minimizing environmental impacts.