Grove Smart Agriculture Kit with Raspberry Pi 4 - designed for Microsoft FarmBeats for Students

• Grove Smart Agriculture Kit w/o Raspberry Pi 4 for Microsoft FarmBeats

• Offers new tools for STEM Education learners

• Easy-to-use and low-cost hardware kit

• Provides real-time data collection

• Build your own Machine Learning models

• Introduce Microsoft responsible AI framework

Microsoft FarmBeats for Students is an interactive STEM education program, that provides teachers and students with tools to explore big data, artificial intelligence (AI), machine learning and IoT technologies.

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.

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. Seeed is honored and pleased to collaborate with Microsoft in the FarmBeats for Students Program (FBFS), which brings these modern tools into the hands of today's learners.

How This Kit Work with Software to Realize Data-Driven Farming

With the combination of software, hardware kit, and curricula resources, the students get a hands-on and immersive experience in the process of learning, to learn about sensor technology, how the changes of data collected from different sensors affect the growth of the crops; thus, they understand soil condition and crop health, etc., and make better decisions with data-driven insights.

To Collect Data

This Grove Smart Agriculture Kit is a hardware kit that consists of an array of multiple sensors measuring soil temperature, soil moisture, sunlight, and air temperature & humidity and other parameters that are crucial for plant growth. With a relay, you can also configure the hardware kit with other hardware modules, to further extend the function from monitoring to controlling such as turn on/off the switch for irrigation or turn on/off the lighting.

Students assemble a Raspberry Pi equipped with atmospheric and environmental sensors to understand their soil's health and the environmental parameters that affect plant growth, then to analyze the data, and make decisions. The student-built IoT devices connect to custom Excel workbooks that collect real-time data using Excel's Data Streamer. They can see the visualized data and further analyze it, thus they can gain insights and make data-driven decisions for their crops.

At the same time, this hands-on experience enables students to learn about AI, Machine learning, data science, and the Internet of Things (IoT) by building a garden monitoring system. With Lobe.ai, students are introduced to building their own Machine Learning models. They build, train, and apply machine learning models to predict nutrient deficiencies in their plants, and identify pests in their garden. There are activities where students set up an agent and others where they 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.