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    About

    Leandro Rodrigues (LR) received a BS (distinguished as best student in the year 2018/2019) and a MSc in Agricultural Engineering at Sciences Faculty - University of Porto (FCUP) and is now attending the 1st year of the PhD in Agrarian Sciences, at the same institution. Recently became Research Assistant at 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 Digital Twin (DT) and Digital Phenotyping (DP) to support better agricultural practices. LR's research interests include various topics in Agronomy and Agricultural Engineering mostly focused on Precision Agriculture (PA) and the implementation of Robotics, Artificial Intelligence and Computer Vision in an agricultural context for crop monitoring and phenotyping. Throughout his academic journey, held the role of Member of the FCUP Pedagogic Council and Member of the MSc Monitoring Commission. He participated in one INESC TEC project, aiding the development of a low-cost cartesian robot for phenotyping, the framework for his Master's Thesis entitled "PixelCropRobot, low-cost robotic prototype for phenotyping in vegetable crops" which was awarded a final grade of 19/20. LR was the co-author of 1 scientific publication published in IEEEXplore (listed in SCOPUS), presented in IRIA 2021. Among other outputs, it is worth mentioning a poster presented at the 13th Young Research Meeting of the University of Porto (IJUP). LR is a member of IAAS Porto Comitee (International Association of Students of Agriculture) since 2017. In 2018, LR organise the International Conference of Youth in Agriculture. From 2019 to 2021 held the role of Director, during that period, he entered the "24H Agriculture by Syngenta" competition and was part of the team responsible for organising the "II Journey of Agronomic Engineering FCUP" event. He also participated in the International Forum of Agricultural Robotics 2020 and attended the Sparkle Curse Entrepreneurship for Sustainable Precision Agriculture by Universitá degli Studi di Firenze and the International Summer School Agricultural Robotics organised by PhenoRob, which allowed him to enhance his knowledge in the Agriculture 4.0 field. He holds a Certificate of Pedagogical Skills (nº F694366/2020) and since 2019 he is a Student Member of the Order of Engineers (nº 86117).

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    2023

    Toward grapevine digital ampelometry through vision deep learning models

    Authors
    Magalhaes S.C.; Castro L.; Rodrigues L.; Padilha T.C.; De Carvalho F.; Dos Santos F.N.; Pinho T.; Moreira G.; Cunha J.; Cunha M.; Silva P.; Moreira A.P.;

    Publication
    IEEE Sensors Journal

    Abstract

    2023

    Computer Vision and Deep Learning as Tools for Leveraging Dynamic Phenological Classification in Vegetable Crops

    Authors
    Rodrigues, L; Magalhaes, SA; da Silva, DQ; dos Santos, FN; Cunha, M;

    Publication
    AGRONOMY-BASEL

    Abstract
    The efficiency of agricultural practices depends on the timing of their execution. Environmental conditions, such as rainfall, and crop-related traits, such as plant phenology, determine the success of practices such as irrigation. Moreover, plant phenology, the seasonal timing of biological events (e.g., cotyledon emergence), is strongly influenced by genetic, environmental, and management conditions. Therefore, assessing the timing the of crops’ phenological events and their spatiotemporal variability can improve decision making, allowing the thorough planning and timely execution of agricultural operations. Conventional techniques for crop phenology monitoring, such as field observations, can be prone to error, labour-intensive, and inefficient, particularly for crops with rapid growth and not very defined phenophases, such as vegetable crops. Thus, developing an accurate phenology monitoring system for vegetable crops is an important step towards sustainable practices. This paper evaluates the ability of computer vision (CV) techniques coupled with deep learning (DL) (CV_DL) as tools for the dynamic phenological classification of multiple vegetable crops at the subfield level, i.e., within the plot. Three DL models from the Single Shot Multibox Detector (SSD) architecture (SSD Inception v2, SSD MobileNet v2, and SSD ResNet 50) and one from You Only Look Once (YOLO) architecture (YOLO v4) were benchmarked through a custom dataset containing images of eight vegetable crops between emergence and harvest. The proposed benchmark includes the individual pairing of each model with the images of each crop. On average, YOLO v4 performed better than the SSD models, reaching an F1-Score of 85.5%, a mean average precision of 79.9%, and a balanced accuracy of 87.0%. In addition, YOLO v4 was tested with all available data approaching a real mixed cropping system. Hence, the same model can classify multiple vegetable crops across the growing season, allowing the accurate mapping of phenological dynamics. This study is the first to evaluate the potential of CV_DL for vegetable crops’ phenological research, a pivotal step towards automating decision support systems for precision horticulture.

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