Team Leader
Aparna Pushkaran Ajitha
Texas A&M University
Department of Animal Science
aparnapushkaran@tamu.edu
Project Type
Research
Who Can Join
Graduate Students, Masters Students, Undergraduate Students
Project Description
This project focuses on developing an integrated data collection and analytics framework to improve resource efficiency in dairy production systems through the Water-Energy-Food (WEF) Nexus approach. The work involves the installation, configuration, calibration, troubleshooting, and maintenance of ultrasonic water flow meters and electrical energy meters to continuously monitor water and electricity consumption at commercial dairy farms. Responsibilities include configuring sensor hardware, verifying measurement accuracy, setting communication and logging parameters, exporting historical datasets, diagnosing connectivity and data acquisition issues, and maintaining reliable long-term data collection. The project also requires integrating data from multiple sources, including water flow meters, energy meters, milk production records, environmental sensors, and farm management systems.
Collected data are cleaned, validated, synchronized, and processed using statistical and computational techniques. Machine learning models are developed to predict resource use, estimate production efficiency, detect anomalies, and identify factors affecting water and energy consumption. The project includes feature engineering, exploratory data analysis, model development, validation, and performance evaluation using appropriate regression and classification algorithms.
The research supports the development of decision-support tools for sustainable dairy farm management by combining sensor technologies, data analytics, and artificial intelligence. Knowledge and skills required include sensor installation and configuration, ultrasonic flow meter operation, electrical energy monitoring systems, data logging and exporting, database management, Python/R programming, statistical analysis, machine learning, data visualization, troubleshooting instrumentation, and interpretation of resource efficiency metrics. The overall objective is to improve water-use efficiency, energy-use efficiency
Team Needs
We are seeking motivated undergraduate and graduate students interested in smart agriculture, precision livestock farming, artificial intelligence, sensor technologies, and data analytics. Team members will contribute to the installation, configuration, calibration, and troubleshooting of ultrasonic water flow meters and electrical energy meters deployed on commercial dairy farms. Responsibilities include assisting with sensor maintenance, exporting and organizing datasets, verifying data quality, and supporting field data collection. Students with experience or an interest in Python, R, MATLAB, machine learning, statistics, database management, data visualization, or IoT systems are encouraged to apply. Experience with agricultural engineering, animal science, electrical systems, environmental monitoring, or computer science is also beneficial. Ideal team members should possess strong analytical and problem-solving skills, attention to detail and an interest in interdisciplinary research. This project provides opportunities to gain hands-on experience with sensor technologies, large-scale agricultural datasets, machine learning model development, and sustainability research while contributing to decision-support tools for improving water, energy, and production efficiency in dairy systems.
Special Opportunities
Students participating in this project will have the opportunity to contribute to interdisciplinary research focused on sensor evaluation and selection for Water–Energy–Food (WEF) Nexus monitoring in commercial dairy farms. Team members will gain hands-on experience in evaluating ultrasonic flow meters, energy meters, sensor integration, data acquisition, data quality assessment, and machine learning applications for agricultural systems. Outstanding contributions may lead to co-authorship on peer-reviewed journal manuscripts, conference abstracts, and presentations at regional and national scientific meetings. Students will also have opportunities to develop technical skills in sensor validation, data analysis using Python or R, statistical modeling, and AI-driven decision-support systems. Participants will collaborate with faculty and graduate researchers while working on real-world sustainability challenges in precision livestock farming. This project provides valuable research experience for students interested in graduate school, careers in agricultural engineering, data science, artificial intelligence, precision agriculture, environmental sustainability, and smart farming technologies.