Contact
- b.caixetaleme(at)ufl.edu
- Blueberry Breeding Program
University of Florida
2550 Hull Rd Gainesville, FL 32611
I am currently a Postdoctoral Associate, responsible for the Smart Fruit Phenomics Team in the Blueberry breeding and Genomics Laboratory at University of Florida. The team studies methods of implementing engineering solutions for acquiring multimodal phenotypic data in crops, which can be used for assisting breeders and growers obtaining faster and more reliable data.
My research interests are centered around the utilization of robotics to enhance phenomics and provide biologists with more accurate data. Moreover, I am interested in extracting meaningful information from this data to enhance plant breeding through the application of data science techniques, including statistical methods and machine learning for data detection and clustering.
Ph.D. in Intelligent Interaction Systems, 2020
University of Tsukuba
M.Eng. in Mechanical Engineering, 2016
University of Taubate
Interests are related on how we can use robotics for improving phenomical data and provide biologists with more accurate information. It involves the development of mechanical devices to percuss crops, the implementation of computer vision techniques to track objects and to manipulate fruits. Moreover, we have interest on how to extract meaningful information from those data to improve plant breeding by applying data science techniques. It includes the use of statistical methods and machine learning techniques for detecting and clustering data. Our ultimate target is to develop technological solutions for smart cropping which can contribute to faster evaluation of the phenotype and the diagnosis of diseases.
Employing AI and computer vision to enhance high-throughput blueberry phenotypic data. Our research focus involve implementing new models for detecting berries amidst complex foliage and forecasting optimal harvest points. Moreover, we are interested on methods for deploying these models in autonomous vehicles to speed up data collection.
Leveraging controlled environments and optimized growth conditions to rapidly develop improved plant varieties with desirable traits. Improving the blueberry industry by shortening breeding timelines and enhancing adaptability to changing conditions.
we have positions for high motivate students to join out interdisciplinary team. Most positions are related to engineering/computer science background but other fields are also encouraged to apply. Positions at this moment are for OPS students.
Working with machine learning and image processing for blueberries classification
Working with design and development of mechanical and electrical systems for innovative projects.