Research & Projects

Project 1

Project 1: High-Resolution Vineyard Nutrient Management

This project focused on developing a high-resolution nutrient management tool for vineyards, specifically targeting nitrogen detection in grapevines. Conducted at the Digital Agriculture Lab, University of California, Davis, and funded by USDA NIFA, this research aimed to optimize fertilizer management practices to promote sustainable agriculture. My role involved integrating hyperspectral imaging, machine learning algorithms, and radiative transfer models to improve the accuracy of nutrient detection.

Objectives

Develop algorithm to detect nitrogen levels in grapevine leaves and canopies.
Integrate Unmanned Aerial Systems (UASs) to map nutrient variability.
Optimize the data processing pipeline for field deployment and agricultural decision-making.

Results

Published findings in Remote Sensing of Environment and Computers and Electronics in Agriculture, showcasing the potential for large-scale adoption.
Pioneered a pathway to make machine learning models for nutrient retrieval more generalizable and scalable, enabling broader applications in precision agriculture.
Provided actionable insights to growers for improving fertilizer application, reducing environmental impact, and boosting crop yields.

Project 2

Land Cover Change Trends in Ghana Using Satellite Remote Sensing

This project analyzed 18 years of satellite data to study land cover changes in Ghana, focusing on environmental degradation and agricultural expansion. Completed during my undergraduate studies at the Center for Remote Sensing, University of Florida, the research was funded by NASA and USAID and earned 2nd place in the K.K. Barnes Student Paper Competition

Objectives

Use MODIS satellite imagery and Google Earth Engine to create time-series datasets for monitoring land cover changes.
Identify trends in urbanization, deforestation, and agricultural expansion to support sustainable land management.

Results

Highlighted regions experiencing rapid environmental degradation, influencing local policy recommendations.
Awarded 2nd place in American Society of Agricultural and Biological Engineers (ASABE) K.K. Barnes Student Paper Award for excellence in research and technical writing

Project 3

Data-Driven Marketing and User Engagement at JOBTOPGUN

As the Co-Lead of the Data Science Department at a leading job search website in Thailand, I implemented data-driven tools to improve marketing efficiency and user engagement. This project was conducted in a corporate setting, focusing on applying machine learning models and ETL pipelines to support business growth

Objectives

Develop a scalable data pipeline to process and analyze user behavior for targeted marketing.
Implement A/B testing and analytics dashboards to monitor campaign performance.
Optimize email marketing strategies to enhance engagement while minimizing costs.

Results

Delivered insights that boosted client acquisition and retention, supporting sustainable business growth.

Publications & Conference Presentations

Chakraborty, M., Pourreza, A., Sirapoom Peanusaha, Farajpoor, P., Khalsa, S. D. S., & Oswald, D. (2025). Integrating hyperspectral radiative transfer modeling and machine learning for enhanced nitrogen sensing in almond leaves. Computers and Electronics in Agriculture, 234, 110195. https://doi.org/10.1016/j.compag.2024.110195
Pourreza, A., Kamiya, Y., Sirapoom Peanusaha, Jafarbiglu, H., Moghimi, A., & Fidelibus, M. W. (2025). Nitrogen retrieval in grapevine (Vitis vinifera L.) canopy by hyperspectral imaging. Computers and Electronics in Agriculture, 229, 109717. https://doi.org/10.1016/j.compag.2024.109717
Sirapoom Peanusaha. (2025). Development of transferable grapevine nitrogen retrieval algorithm.
Sirapoom Peanusaha, Pourreza, A., Kamiya, Y., Fidelibus, M. W., & Chakraborty, M. (2024). Nitrogen retrieval in grapevine (Vitis vinifera L.) leaves by hyperspectral sensing. Remote Sensing of Environment, 302, 113966. https://doi.org/10.1016/j.rse.2023.113966
Pourreza, A., Chakraborty, M., Farajpoor, P., & Sirapoom Peanusaha. (2024). Nitrogen monitoring in specialty crops of California: Case studies in almond and grape. EARSeL Workshop on Imaging Spectroscopy 2024.
Chakraborty, M., Oswald, D., Sirapoom Peanusaha, & Pourreza, A. (2023). Nitrogen retrieval by spectral sensing in almonds. IEEE WHISPER 2023.
Kamiya, Y., Pourreza, A., Sirapoom Peanusaha, & Fidelibus, M. W. (2023). Grapevine nitrogen retrieval by hyperspectral sensing at the leaf and canopy level. IVES Conference Series: GiESCO 2023, Cornell University, Ithaca, NY, USA.
Sirapoom Peanusaha, Pourreza, A., Fidelibus, M. W., Jafarbiglu, H., & Ramirez, G. Z. (2022). Development of a machine learning approach towards grape leaf nitrogen content detection using hyperspectral data. ASABE Annual International Meeting, Paper No. 2200842.
Sirapoom Peanusaha, Judge, J., Muneepeerakul, R., & Worrall, G. (2020). Identifying causal relationships for land cover changes in Ghana using satellite remote sensing. [Competition manuscript, K.K. Barnes Student Paper Competition]. Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA.

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