AgriTech A.I.

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Agriculture and the environment face mounting challenges—from feeding a growing population to coping with climate change and resource scarcity. The theme “AgriTech A.I.: Smart Farming & Environmental Intelligence” applies artificial intelligence to both Smart Farming and Environmental Intelligence, helping improve food production while also protecting ecosystems.

Projects in this theme explore tools like crop disease detection, predictive yield models, precision irrigation, and environmental forecasting. By merging agriculture with environmental intelligence, students design systems that balance productivity with sustainability, showing that technology can serve both farmers and the planet.

Smart Farming:

ARNEJO, Zenith O., MSCS 2018
Automated detection and counting of coconut trees in aerial images
BORJA, Adrian A., MSAE 2018 (Advisory Committee)
Development of a machine vision assisted mechatronic seed meter for corn (Zea mays L.)
DE GRANO, Alona V., BSCS 2003
Configuring a classifying neural network for tomato grading
MARQUEZ, Carlo Martin A., BSCS 2008
Wine cultivar recognition using committee machines
MAXIMO, Krismagne M., BSCS 2012
Computerized grading system of Mangifera indica L. (pico mango variety) using artificial neural networks and image processing
MORES, Jemarjo L., BSCS 2011 (with Vendiola, Bethuel Rein M.)
Water stress approximation through geometrical computations and image analysis of Vigna radiata grown under controlled conditions
OLIVA, Angela J., BSCS 2011 (with Rogado, Reli Ann Faye L., advised by Prof. Margarita Carmen S. Paterno)
UPLB-DA DNA Barcoding Project Library: An online information system for the Philippine Livestock and Poultry Sector
PASCUA, Gauven Roy M., BSCS 2018
Operation Skuld: An online database for veterinary clinics
PERALTA, Caroline Natalie M., MSCS 2016
Modeling and matching rice seed shapes using uniform cubic B-splines
REVILLA, Brian Joshua M., BSCS 2018
Approximating DSSAT-CSM growth outputs using deep neural networks
VENDIOLA, Bethuel Rein M., BSCS 2011 (with Mores, Jemarjo L.)
Water stress approximation through geometrical computations and image analysis of Vigna radiata grown under controlled conditions
ZARSUELA, Allan L., BSCS 2006
Automatic egg grading system using image processing and committee machines

Environmental Intelligence:

BALANGCOD, Ashlyn Kim D., MSCS 2019
Automated identification of plant species using leaf venations
CAPUNPON, Ramon Leonardo C., BSCS 2020
Support vector regression on GIS and map data for landslide susceptibility of Laguna, Philippines
CORALDE, Lizhier B., BSCS 2003 cum laude
Browser-based weather maps
DINGLE, Daryl Jed M., BSCS 2008
Visualization of TDMA-based sensor networks
MILANO, Angelo Luis N., BSCS 2024
A Computational Workflow for the Discrimination, Identification, and Counting of Mosquitoes
QUELISTE, Miyah D., MSCS 2020
Development of an autonomous surface vehicle for water quality monitoring

Students are invited to take part in shaping the future of AgriTech A.I. by contributing new ideas, tools, and applications that extend beyond what has been done before. Whether the interest lies in coding intelligent algorithms, developing decision-support systems, or integrating sensors with data platforms, there is space in this theme for innovation that makes a tangible impact. By joining this research theme, students help advance technologies that not only sustain food production but also safeguard the environment on which agriculture depends.