Flávia Souza, Ph.D.

• Develop an integrated agricultural monitoring system combining satellite imagery, weather, crop phenology, field observations, and ground-based camera data. • Build data pipelines, APIs, and geospatial workflows to support agricultural analytics and regional-scale monitoring. • Contribute to the design and development of an interactive digital platform for data-driven decision-making in crop production systems. • Integrate remote sensing, field-level data, and computational tools to improve agricultural monitoring and support practical management decisions. • Collaborate with multidisciplinary teams across agricultural engineering, remote sensing, and digital agriculture.

• Conducted on-farm agricultural trials focused on data-driven decision-making and precision management. • Developed machine learning models to support management optimization, including nitrogen-related decisions. • Analyzed spatial variability, yield response, and economic return to evaluate management strategies under field conditions. • Translated complex agronomic and economic data into practical recommendations for growers and applied research teams. • Supported precision agriculture initiatives connecting field experimentation, profitability, and agronomic performance.

• Participated in applied agricultural research integrating field data, crop management, and precision agriculture analytics. • Supported projects involving data interpretation, agronomic experimentation, and technology-driven decision-making. • Collaborated with U.S.-based research teams on practical solutions for agricultural systems.

• Contributed to research projects in precision agriculture and digital crop monitoring. • Worked with UAV imagery, machine learning, and applied agronomic datasets to address crop production challenges. • Collaborated in international research activities involving agricultural innovation, data analysis, and image-based crop assessment.

• Co-founded and led an AI-focused research group in agriculture, driving initiatives in precision agriculture, data analysis, and innovation. • Established the group’s identity, research направления, and strategic vision from inception. • Mentored and guided students in research projects, fostering technical and scientific development. • Led organization of seminars, workshops, and knowledge-sharing events, strengthening academic and industry connections. • Represented the group in public engagements, including talks, interviews, and outreach activities. • Developed collaborations and expanded networks across academia and the agtech ecosystem. • Managed group operations, communications, and project execution.

• Conducted analysis of environmental compliance cases involving fauna, flora, and fisheries, supporting regulatory enforcement and sustainability initiatives. • Assisted in technical evaluations and documentation for environmental monitoring and inspection programs. • Supported field operations and tracked technical surveys related to biodiversity and natural resource management. • Contributed to environmental reporting and data organization to support policy implementation and decision-making. • Engaged with stakeholders and the public to support transparency and environmental governance processes.

• Coordinated field operations and logistics across five Brazilian states, optimizing technician scheduling and project execution. • Supported supplier engagement within the Nestlé Good Agricultural Practices Program (BPF), ensuring compliance with quality standards. • Monitored budgets and operational performance, contributing to cost control and process efficiency. • Facilitated communication between field teams, clients, and stakeholders to support audit readiness and project delivery.

Precision Agriculture and Remote Sensing Applications for crop management.

Ph.D. focused on artificial intelligence, computer vision, and UAV-based crop analysis for soybean plant quantification and stand-gap identification in production systems in Brazil and the United States. Research included international appointments at Louisiana State University and the University of Connecticut. • AI and computer vision for soybean stand assessment • UAV RGB and multispectral imagery • Precision agriculture and digital agriculture • International research experience in the U.S.

Research focused on the application of artificial intelligence to model agronomic parameters, with emphasis on precision agriculture, geospatial analysis, and digital tools for soybean systems.

Applied projects in erosion risk assessment using geotechnologies and field experimentation with organomineral fertilization in sugarcane, with focus on crop productivity, soil management, and sustainability.