Week 1

Day 1: Program initiation

Overview: The day began with everyone meeting at the TAMUCC Natural Resource Center. After a BBQ lunch together, 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 was done. Then, we went on a campus tour and concluded the day with a visit to the I-Create lab, where the student cohort 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.

Day 2: Data Science Workshop and Project Presentation/Form Teams 

Overview: The day began with an engaging data science workshop aimed at introducing students to essential features in data analysis and data science methodologies. Students delved into the fundamentals of Python programming language, focusing on libraries such as Pandas for data manipulation and analysis. 

Following the workshop, students were informed on the target goals of this cohort’s mission and were advised to form teams and brainstorm ideas for a project topic.

Objective: By the day's end, students gained foundational data science knowledge, such as Python basics and key libraries like Pandas. 

The afternoon session helped foster interdisciplinary insights for their internship projects.

Day 3-5: Introduction to IoT and IoT Applications in Agriculture

Overview: This day’s workshops led by Dr. Mehdi Sookhak, PhD candidate Azim Rezaei, and undergraduate student Raj Patel was dedicated to introducing students to the realm of Internet of Things (IoT) and its applications in agriculture. They delved into the fundamentals of IoT technology, understanding its role in agricultural practices. Specifically, students explored the Raspberry Pi 5 and its components, gaining hands-on experience in assembling and configuring the microcontroller for agricultural applications.

Objective: By the end of the day, students had acquired foundational knowledge in IoT and its potential applications in agriculture. They demonstrated proficiency in identifying and understanding the components of the Raspberry Pi 5, laying the groundwork for future projects and innovations leveraging IoT technology in agricultural settings.