TAMUCC AgTech REEU

Lead Mentor: Dr. Mehdi Sookhak

Project Background: The emerging IoT brings new opportunities to empower next-generation agriculture. IoT can significantly extend the agricultural workforce with minimum cost and human involvement while maintaining excellent performance.  

Project Description: We will implement a small-scale embedded IoT network consisting of multiple low-power embedded IoT devices. Each embedded IoT device will be equipped with multiple environmental sensors (temperature, humidity, etc.), a microcontroller unit in Raspberry Pi, and a transceiver to enable sensing, processing, and communication. Due to long-term cost, sustainability, and environmental concerns, we will power the devices with energy harvesters that scavenge ambient energy from solar. 

Student Research Activities:

1) Build an IoT embedded system using off-the-shelf electronic components, focusing on how it contributes to the sustainability of IoT deployments.

2) Design smart sensing algorithms for sensors and deploy data preprocessing algorithms on microcontrollers through Raspberry Pi

3) Enable wireless data transmission to an IoT edge server.

Lead Mentor: Dr. Bhandari

Background: Advances in analyzing UAS remote sensing images have contributed to improving our understanding of crop growth and development and yield predictions by the ability to collect high spatial-temporal data on plant morphological traits. However, ground-truth data collection, a key component in UAS applications, is tedious, laborious, and expensive. Automated systems equipped with IoT technology have shown the potential to gather high quality real-time data related to field conditions continuously. We envision that the integration of UAS with a ground-based IoT system will enhance our capability on understanding crop growth and developing management practices.

Project Description: We will investigate challenges and their solutions in the combination of IoT and UAS for predicting crop yield. The IoT sensors will collect site-specific field conditions (for example soil moisture, canopy temperature, air temperature, and relative humidity). The UAS will gather data on canopy height, canopy cover, and vegetation indices. We will feed data from both platforms into mechanistic crop growth models to assess site-specific crop management practices and predict crop yield. This will help students learn the data integration approach from multiple devices, and we can demonstrate how advanced tools and techniques can be used to better understand crop growth and manage them efficiently and effectively.

Student Research Activities: 

1) UAS data collection procedures for agriculture applications. 

2) Hands on training on UAS image processing pipeline for generating significant crop canopy features. 

3) Utilizing extracted canopy features to assess abiotic stress in plants. 

Lead Mentor: Dr. Huang

Background: This project involves a real-time geo-visualization and analytics platform that incorporates data collection, modeling, and visualization. Such a platform is essential for smart farming to present, visualize and analyze spatial and temporal agricultural data. 

Project Description: We will design and build spatial databases to store and manage data gathered from Projects #1 and #2. Leveraging various GIS techniques such as ArcGIS online, we will create a platform to visualize and analyze the collected spatial and temporal data. Real-time solutions including ArcGIS dashboards and automatic scheduling tasks, will be employed for interacting, tracking, and monitoring agricultural data to support smart farming applications. 

Student Research Activities:

1) Design and develop geospatial databases to store and manage spatial data from IoT sensors and UAS. 

2) Create and implement ArcGIS dashboards to enable interactive visualization of spatial and temporal data. 

3) Integrate ArcGIS real-time capabilities to automate, track, and monitor processes tailored to smart farming. 

Lead Mentor: Dr. Mcginty

Background: A key goal of the land grant university system is to provide educational outreach to stakeholders. Texas A&M AgriLife Extension Service is tasked with bringing science-based information and solutions to landowners and managers across the state through its network of 250 county offices.

Project Description: During the spring and summer, county extension agents (CEA’s) provide educational programs and conduct result demonstrations in various row crops in south Texas. Topics of these programs and demonstrations are crop variety selection, pest management, and general crop management.

Student Research Activities: Students will spend time with local CEA’s as they conduct onfarm result demonstrations in cotton, corn, and grain sorghum and learn how these demonstrations help to improve the sustainability and profitability of local producers. Students will also participate in educational programs where they will have the opportunity to present data from Projects 1-3 to local producers.