Germano Moreira
Papers
5
Total Citations
279
H-Index
4
About
Germano Moreira is a researcher specializing in computer vision, deep learning, and agricultural robotics, with a particular focus on automating harvesting and plant monitoring processes in precision agriculture. His work centers on developing robust visual perception systems capable of detecting and classifying crops across varying growth stages, making meaningful contributions to the advancement of autonomous agricultural machinery. Moreira's most impactful contribution is his highly cited 2021 study evaluating Single-Shot MultiBox Detector (SSD) and YOLO architectures for greenhouse tomato detection, which has accumulated 177 citations and stands as a key reference in agricultural deep learning research. Building on this, his 2022 benchmark study comparing deep learning models with HSV colour space approaches for tomato detection and classification has garnered 88 citations, further cementing his authority in the field. He has also contributed the AgRobTomato Dataset, a publicly available resource enabling reproducible research in crop detection. More recently, his work has expanded into pollination biology, applying deep learning to Actinidia flower detection and gender classification — a critical challenge for sustainable crop production. Through rigorous experimentation and open data sharing, Moreira has helped bridge the gap between robotics research and practical agricultural automation.
Research Focus
Key Achievements
Top Papers
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- 4Tomato Detection Using Deep Learning for Robotics Application5 citations · 2021
- 5AgRobTomato Dataset: Greenhouse tomatoes with different ripeness stages2 citations · 2021