Overview: The day began with everyone meeting at the TAMUCC Natural Resource Center at noon. We enjoyed a BBQ lunch together before continuing with a round of introductions, including welcome remarks from CBI, the Department of Computer Science, and AgriLife CC, along with introductions of the project teams, participants, and postdoc/graduate student workers. Afterward, we went on a campus tour and concluded the day with a visit to the I-Create lab, where everyone had their new water bottles custom engraved using a laser engraving machine.
Objective: By the end of the day, students had a clear understanding of the program structure, the resources available, project details, and logistical information. They felt welcomed into the program and were equipped with the necessary knowledge to navigate their internship experience successfully.
Overview: The day began with an introduction to smart agriculture and the role of IoT. Students explored how sensors and microcontrollers can collect and transmit agricultural data, followed by hands-on activities designing energy-efficient IoT systems. Students learned about sensor types for soil, weather, plants, animals, and the environment, along with connectivity options like Wi-Fi and LoRaWAN. The experience concluded with discussions on data analysis, AI applications, and future trends like automation and blockchain in agriculture.
Objective: By the end of the day, students gained foundational understanding of circuitry for IOT devices and their applications in smart agriculture.
Overview: The day began with students developing a virtual soil moisture sensor system using the Tinkercad simulation website, where an LED would light up when dry soil conditions were detected. After successfully simulating the system, students assembled the physical device using real components. Following lunch, the session shifted to an introduction to Python programming and the Raspberry Pi platform. Students also learned about the differences between microcontrollers and microprocessors, deepening their understanding of how each plays a role in smart farming. The day concluded with students completing a functional soil moisture monitoring system that combined an Arduino and Raspberry Pi.
Objective: By the end of the day, students gained hands-on experience building and programming a soil moisture monitor while learning the foundational concepts of Python, Raspberry Pi, and microcontroller-based systems.
Overview: The day focused on expanding students’ smart farming IoT devices by integrating an additional soil moisture sensor and a DHT22 sensor to monitor air temperature and humidity. Students learned how to enhance data collection capabilities and interface multiple sensors with their existing systems. They also created Google Cloud API service accounts and developed Python scripts to run on Raspberry Pi devices, enabling automatic uploading of logged sensor data to the cloud for real-time monitoring and analysis.
Objective: By the end of the day, students gained practical experience in multi-sensor integration and cloud-based data logging using Python and Raspberry Pi.
Overview: This half-day marked the start of the Memorial Day weekend and began with a focused lesson on using the Pandas Python library. Students learned how to import and analyze data collected by their soil and atmosphere monitoring systems completed the previous day. The session emphasized data manipulation techniques to help students draw meaningful insights from their sensor readings, further integrating programming skills with agricultural technology.
Objective: By the end of the day, students developed a foundational understanding of data analysis using Pandas in order to it to interpret environmental data from their IoT monitoring systems later in the program.