About Our Lab

The Ciampitti Lab focuses on digital agriculture research, combining computer vision, data analysis, and traditional crop management systems to advance sustainable agriculture.

Our Mission

At the Ciampitti Lab, our mission is to develop innovative digital agriculture solutions that enable more efficient, sustainable, and productive farming practices. We leverage cutting-edge technologies in computer vision, machine learning, and data analytics, while maintaining a strong foundation in traditional crop science and management systems.

Our interdisciplinary approach brings together experts from agronomy, computer science, engineering, and data science to address complex challenges in modern agriculture. Through collaborative research and engagement with industry partners, we aim to bridge the gap between academic innovation and practical applications in the field.

We are committed to training the next generation of agricultural scientists and technologists through our educational programs and research opportunities for students at all levels.

Research Focus Areas

Agricultural Data Analysis

Leveraging big data approaches to analyze field performance data, weather patterns, and crop responses to various management practices. We develop predictive models and decision support tools to help optimize agricultural operations.

Crop Management Systems

Investigating innovative crop management strategies that enhance yield, quality, and sustainability. Our research focuses on optimizing inputs and management practices through a combination of traditional field research and digital agriculture approaches.

Computer Vision in Agriculture

Using image recognition and processing techniques to monitor crop health, detect diseases, and measure plant growth parameters. Our computer vision research aims to automate the collection of plant phenotypic data and enable early detection of stress conditions.

Our Facilities

Research Facilities

Field research hub with four core facilities: dry lab, wet lab, hybrid lab, and field barn.
Advanced imaging systems including RGB, multispectral, and depth cameras.
Computational resources for training machine learning models, data processing and analysis.
Greenhouse facilities for controlled environment studies.

Interested in Collaborating?

We're always open to new research collaborations, industry partnerships, and student opportunities. Get in touch to discuss how we can work together.