PhD Students
Airton Andrade da Silva - PhD Student | ORCID | Linkedin | Curriculum
CAPES Fellow
"Less loss, more yield: the power of management zones."
Project: Management Zones: Precision and efficiency in soybean harvesting
Objective: To evaluate the impact of creating management zones based on yield prediction on the efficiency of soybean mechanized harvesting, through the application of customized settings aimed at reducing losses.
"AI-powered mechanization: precision, performance, and sustainability in every field."
Project: On-Farm Mechanization Sizing Models with AI
Objective: To develop and apply farm mechanization sizing models assisted by artificial intelligence, aiming at the careful and customized definition of mechanized sets. The approach considers the diversity of agricultural operations to promote efficient farm management, optimize the use of available resources, maximize operational performance, and incorporate sustainable practices that reduce environmental impacts and encourage the responsible use of energy and inputs.
Igor Cristian de Oliveira Vieira - PhD Student | ORCID | Linkedin | Curriculum
CAPES Fellow
"Climate brings chaos. Our job is to find the pattern for future growing seasons."
Project: Agroclimatic and topographic zoning for mechanized peanut cultivation in Brazil and the United States under climate change scenarios
Objective: To develop an agroclimatic and topographic zoning plan for peanut cultivation in Brazil and the United States, aiming to identify and map suitability classes for mechanized farming under current and future climate scenarios, based on different CO₂ emission projections.
Paulo Henrique Cardoso | ORCID | Linkedin | Curriculum
"Turning data into decisions: artificial intelligence to maximize sugarcane yield and quality."
Project: Machine learning and remote sensing: predictive modeling of sugarcane quality
Objective: To develop and validate predictive models based on machine learning, using remote sensing data (from satellites and drones), to estimate sugarcane quality parameters such as Brix, Pol, and purity at different phenological stages, supporting decision-making on the optimal harvest timing and improving overall production efficiency.
Pedro Henrique Nogueira Gusmão - MSc Student | ORCID | Linkedin | Curriculum
CAPES Fellow
"Smart farming for a sustainable and loss-free peanut harvest."
Project: Georeferenced reconstruction of peanut windrows using LiDAR and drone imagery for harvest optimization
Objective: To develop a georeferenced reconstruction system for peanut windrows using mobile laser scanning, static LiDAR, and drone imagery, aiming to generate high-resolution 3D products (DEM, DSM, and orthomosaics) that support machine adjustments, improve operational performance, and reduce losses and environmental impacts.
Rosemary Gay - PhD Student | ORCID | Linkedin | Curriculum
(University of North Carolina - U.S.A.)
"Peanut connecting science, territory, and geopolitics between Brazil and the U.S."
Project: Peanut Powers: Transamerican Scientific Innovations in Brazil and the U.S.
Objetive: To investigate scientific exchanges between Brazil and the U.S. in the peanut agro-industrial sector, analyze how geopolitical relations shape international agricultural science, and document local and global practices of specialists through ethnographic research.
Thiago Caio de Moura Oliveira - PhD Student | ORCID | Linkedin | Curriculum
CAPES Fellow
"In smart agriculture, technology outperforms talent when talent fails to keep up."
Project: Delimitation of Management Zones Using Remote Sensing and Artificial Intelligence for Smart Mechanized Peanut Harvesting
Objective: To estimate the peanut maturation index using remote sensing data and, based on this estimation, define management zones considering the spatial variability of maturation. Artificial intelligence models will be applied to generate more accurate predictions, aiming to optimize smart mechanized harvesting and promote greater efficiency and sustainability in agricultural production.
Vinicius dos Santos Carreira - PhD Student | ORCID | Linkedin | Curriculum
FAPESP Fellow
"High-resolution remote sensing meets tomato farming."
Project: High-resolution remote sensing techniques for tomato crops
Objective: To develop a framework that integrates a range of techniques using UAV imagery for tomato crops. This framework includes: 1. pipeline optimization (transmission, radiometry and geometric corrections), 2. yield and maturity estimation using spectral data and machine learning, 3. yield loss mapping using convolutional neural networks and 4. a pipeline for analyzing sun-view geometry in UAV imagery of vertical canopies.
Students in Secondary and Technical Education
"Embedded precision: monitoring fuel, maximizing performance."
Project: Fuel consumption monitoring system for agricultural machinery using Arduino microcontroller
Objective: To develop an embedded system with Arduino for data collection, processing, and recording to monitor fuel consumption, aiming to accurately and in real-time track the amount of fuel used during agricultural operations.