Marcelo Barbosa

Marcelo Barbosa

Marcelo Barbosa’s program focuses on integrating cutting-edge technologies to advance and support modern agricultural production systems. He is particularly interested in bridging the gap between emerging technologies and real-world agricultural applications, ensuring that innovative tools are practical, scalable, and accessible for producers. He collaborates closely with industry partners, producers, and interdisciplinary research teams to deliver applied solutions that increase efficiency, reduce resource use, and promote resilient and sustainable crop production.

Research Topics:

  • Remote sensing for crop monitoring (satellite, UAS/drone, and proximal platforms)
  • Artificial intelligence for agricultural applications
  • Predictive and forecasting models for crop yield, quality, and health
  • Robotics and automation in precision agriculture
  • Decision-support systems for data-driven farm management

New Funding/Grant

PI – In-House appropriated research project: Improving Irrigated Crop Management System for Humid and Sub-humid Climates. United States Department of Agriculture Agricultural Research Service (USDA ARS).
PI – Multi-Sensor and AI-Based Monitoring of Irrigation and Nitrogen Dynamics in Cotton Production Systems in Southeast Missouri. (USDA ARS subproject). Awarded

2026 (1)
CARREIRA, V. S.; ALMEIDA, S. L. H.; ZUDE-SASSE, M.; BARBOSA JÚNIOR, M. R.; TOMMASELLI, A. M. G.;
SILVA, R. P. Close-range and remote sensing applications in tomato crops: a systematic review. European
Journal of Remote Sensing. In press

2025 (6)
MARTINS, J. V. S.; BARBOSA JÚNIOR, M. R.; SALES, L. A.; SANTOS, R. G.; RIBEIRO, W. S.; OLIVEIRA, L. P.
Automated crop measurements with UAVs: Evaluation of an AI-driven platform for counting and biometric
analysis. Agriculture.
https://doi.org/10.3390/agriculture15212213

BARBOSA JÚNIOR, M. R.; SANTOS, R. G.; SALES, L. A.; MARTINS, J. V. S.; SANTOS, J. G.; OLIVEIRA, L. P.
Designing and implementing a robotic system to support spraying drone operations. Actuators.
https://doi.org/10.3390/act14080365

BARBOSA JÚNIOR, M. R.; SALES, L. A.; SANTOS, R. G.; VARGAS, R. B. S.; OLIVEIRA, L. P. Forecasting yield
and market class of Vidalia sweet onions: A UAV-based multispectral and texture data-driven approach.
Smart Agricultural Technology.
https://doi.org/10.1016/j.atech.2025.100808

BARBOSA JÚNIOR, M. R.; SANTOS, R. G.; SALES, L. A.; VARGAS, R. B. S.; DELTSIDIS, A.; OLIVEIRA, L. P. Image-
based and ML-driven analysis for assessing blueberry fruit quality. Heliyon.
https://doi.org/10.1016/j.heliyon.2025.e42288

BRITO FILHO, A.L.; CARREIRA, V. S.; SOUZA, J. B. C.; ALMEIDA, S. L. H.; BARBOSA JÚNIOR, M. R.; CARNEIRO,
F. M.; SILVA, R. P. Deep convolutional networks based on lightweight YOLOv8 for the detection and estimation
of peanut losses from images of the post-harvest environment. Computers and Electronics in Agriculture.
https://doi.org/10.1016/j.compag.2025.110282

SOUZA, F. L. P.; SHIRATSUCHI, L. S.; DIAS, M. A.; BARBOSA JÚNIOR, M. R.; SETIYONO, T.; CAMPOS, S. A
neural network approach employed to classify soybean plants using multi-sensor images. Precision
agriculture.
https://doi.org/10.1007/s11119-025-10229-1

2024 (7)
BARBOSA JÚNIOR, M. R.; SANTOS, R. G.; SALES, L. A.; OLIVEIRA, L. P. Advancements in agricultural ground
robots for specialty crops: An overview of innovations, challenges, and prospects. Plants.
https://doi.org/10.3390/plants13233372

BARBOSA JÚNIOR, M. R.; MOREIRA, B. R. A.; CARREIRA, V. S.; BRITO FILHO, A. L.; TRENTIN, C. SOUZA, F. L.
P.; TEDESCO, D.; SETIYONO, T.; FLORES, J. P.; AMPATZIDIS, Y.; SILVA, R. P.; SHIRATSUCHI, L. S. Precision
agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and
adoption. Computers and Electronics in Agriculture.
https://doi.org/10.1016/j.compag.2024.108993

BARBOSA JÚNIOR, M. R.; MOREIRA, B. R. A., DURON, D., SETIYONO, T., SHIRATSUCHI, L. S., SILVA, R. P.
Integrated sensing and machine learning: Predicting saccharine and bioenergy feedstocks in sugarcane.
Industrial Crops and Products.
https://doi.org/10.1016/j.indcrop.2024.118627

CANATA, T. F.; BARBOSA JÚNIOR, M. R.; OLIVEIRA, R. P.; FURLANI, C. E. A.; SILVA, R. P. AI-driven prediction
of sugarcane quality attributes using satellite imagery. Sugar Tech.
https://doi.org/10.1007/s12355-024-01399-9

KAZAMA, E. H.; TEDESCO, D.; CARREIRA, V. S.; BARBOSA JÚNIOR, M. R.; OLIVEIRA, M. F.; FERREIRA, F. M.;
JUNIOR, W. M.; SILVA, R. P. Monitoring cokee fruit maturity using an enhanced convolutional neural network
under dikerent image acquisition settings. Scientia Horticulturae.
https://doi.org/10.1016/j.scienta.2024.112957

MOREIRA, B. R. A.; CRUZ, V. H.; BARBOSA JUNIOR, M. R.; LOPES, P. R. M.; SILVA, R. P. Fuel-flexible biomass
ok-gassing: The impact of antioxidant spent cokee grains on emissions of CO2, CO, CH4, and VOCs, physical
deposits, and combustion in wood pellets. Industrial Crops and Products.
https://doi.org/10.1016/j.indcrop.2023.117748

MOREIRA, B. R. A.; MARRA, T. M.; SILVA, E. A.; BRITO FILHO, A. L.; BARBOSA JÚNIOR, M. R.; SANTOS, A. F.;
SILVA, R. P.; VELLIDIS, G. Advancements in peanut mechanization: Implications for sustainable agriculture.
Agricultural Systems.
https://doi.org/10.1016/j.agsy.2024.103868

Ongoing/New Projects

1. Integrating Multi-Sensor Systems and AI for Monitoring Peanut Variety Performance: This project introduces a range of sensors and platforms for intensive monitoring of peanut crops. The approach focuses on modeling plant growth patterns and yield potential using sensor-derived data combined with AI-based analysis.