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About

About

Germano Moreira (GM) received a B.S. and an M.Sc. in Agricultural Engineering at Sciences Faculty - University of Porto (FCUP) and is now attending the 1st year of the PhD in Agricultural Sciences, at the same institution. Recently became a Research Assistant at the Institute for Systems and Computer Engineering, Technology and science (INESC TEC), TEC4AGRO-FOOD, Centre for Robotics in Industry and Intelligent Systems (CRIIS), as a member of the multidisciplinary team of the Laboratory of Robotics and IoT for Smart Precision Agriculture and Forestry, where he will integrate several projects and develop research on Artificial Intelligence (AI) and Computer Vision (CV) to support different agricultural operations. GM's research interests include various topics in Agronomy and Agricultural Engineering mostly focused on Precision Agriculture (PA) and the implementation of Robotics, AI, CV, and Deep Learning in an agricultural context for Crop monitoring and phenotyping. Throughout his academic journey, he participated in two EU-funded INESC TEC projects: AGRINUPES (Water JPI) - Integrated monitoring and control of water, nutrients and plant protection products towards a sustainable agricultural sector - and ROBOCARE (FEDER) - Robotic Platforms for IntelligentPrecision Crops - the latter being the framework for his master's thesis entitled "Tomato Robotic Harvesting in Protected Horticulture: Machine Learning Techniques for Fruit Detection and Classification" which was awarded a final grade of 20/20. GM was the (co)author of 4 scientific publications (ResearchGate) with a total of 14 citations - 3 articles and 1 book chapter - published in journals listed in JCR-WoS and/or SCOPUS, with the most recent publication as the first author being selected for the cover story of Issue 2, Volume 12, of MDPI'sjournal Agronomy (https://www.mdpi.com/2073-4395/12/2/356). Among other outputs, it is worth mentioning aconference poster presented at the 13th Young Research Meeting of the University of Porto (IJUP). GM is amember of IAAS (International Association of Students of Agriculture and Related Sciences), holding the roleof president of the local assembly of the Porto committee from 2019 to 2021. During that period, he enteredthe "24H Agriculture by Syngenta" competition and was part of the team responsible for organising the "IIJourney of Agronomic Engineering FCUP" event. He also participated in events such as "The Digital (R)evolution in Agro-Food and Forestry", "Mechanization and Automation in Slope Viticulture" and attendedthe "International Summer School Agricultural Robotics", organised by PhenoRob (https://www.phenorob.de/),which allowed him to enhance his knowledge in the Agriculture 4.0 field.

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Publications

2022

Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato

Authors
Moreira, G; Magalhaes, SA; Pinho, T; dos Santos, FN; Cunha, M;

Publication
AGRONOMY-BASEL

Abstract
The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.