Zarembo, Imants

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Preferred name
Zarembo, Imants
Official Name
Zarembo, Imants
Alternative Name
Imants Zarembo
Zarembo, I
Main Affiliation
Email
imants.zarembo@rta.lv
ORCID
Scopus Author ID
56326264300
Researcher ID
AAI-1965-2020

Research Output

Now showing 1 - 3 of 3
  • Publication
    Digital twin modelling for smart fruit-growing: eco-cyber-physical system 4+1 architecture
    (2023)
    Kodors, Sergejs
    ;
    Zarembo, Imants 
    ;
    Ginta Majore
    ;
    Edgars Rubauskis
    ;
    Lienite Litavniece
    The continuous evolution of technology and industrial revolutions provides new horizons for the application of smart solutions in every aspect of human lives. At the same time, it causes new social and engineering challenges, which require appropriate methodologies and solutions to overcome them. A smart orchard is an example of an eco-cyber-physical system. Modelling of an eco-cyber-physical system is more complex than simple software development because the complexity quickly grows together with the amount of interconnected components. The difficulties come together with various fruit tree species (in our case apples, pears, and cherries), related technologies and orchard systems. Such systems differ even in the frame of one species like apple trees (rootstocks effect on the tree size; planting density; tree canopy training systems, etc.) and some production risk-reducing technologies (rain and hail, against birds and insects protecting covering systems), as well as agricultural machinery movement in the site. An additional accelerator of complexity growth is the development of eco-cyber-physical and data-driven decision-making paradigms, which propose business-driven development considering ecological and technical aspects together within cyberspace. As a result, the product of synergy is a digital twin paradigm, which provides digital mirrors for artificial intelligence to monitor and manipulate the physical world considering environmental aspects. This article presents the digital twin “4 + 1” model of a smart orchard, which is described using modern visual notations like 4EM for project view, ARTSS for logical view, OPM for process view, IoT-adapted UML component diagram for physical view and spatial map for deployment view. The proposed methodology offers a roadmap for design and development of smart solutions in fruit-growing to predict the yield and potential of income generation in the early stages of the season.
  • Publication
    RAPID PROTOTYPING OF PEAR DETECTION NEURAL NETWORK WITH YOLO ARCHITECTURE IN PHOTOGRAPHS
    (2023)
    Kodors, Sergejs
    ;
    Marks Sondors
    ;
    Gunārs Lācis
    ;
    Edgars Rubauskis
    ;
    Apeināns, Ilmārs
    ;
    Zarembo, Imants 
    Fruit yield estimation and forecasting are essential processes for data-based decision-making in agribusiness to optimise fruit-growing and marketing operations. The yield forecasting is based on the application of historical data, which was collected in the result of periodic yield estimation. Meanwhile, the object detection methods and regression models are applied to calculate yield per tree. The application of powerful neural network architectures for rapid prototyping is a common approach of modern artificial intelligence engineering. Meanwhile, the most popular object detection solution is YOLO architecture. Our project team collected the dataset of fruiting pear tree photographs (Pear640) and trained YOLOv5m with mAP@0.5 95% and mAP@0.5:0.95 56%. The obtained results were compared with other YOLOv5-7.0 and YOLOv7 models and similar studies.
  • Publication
    DIGITAL TWIN: ORCHARD MANAGEMENT USING UAV
    (2023)
    Zarembo, Imants 
    ;
    Kodors, Sergejs
    ;
    Apeināns, Ilmārs
    ;
    Gunārs Lācis
    ;
    Daina Feldmane
    ;
    Edgars Rubauskis
    Orchard management can benefit greatly from the use of modern technology to reach higher yields, decrease costs and achieve more sustainable farming. Implementation or such a smart farming approach into orchard management can be realised via application of unmanned aerial vehicles (UAV) for data collection and artificial intelligence (AI) for yield estimation and forecasting. On top of that, a digital twin of the orchard can be implemented to represent the physical system of the orchard in the digital format allowing implement modern data-driven decision-making based on fruit-growing automation.The aim of this study is to present a digital twin based on application of UAV and AI for orchard management that is being developed as part of a research project lzp-2021/1-0134. At this moment, we are developing a user-centred design which is oriented to satisfy horticulture specialists’ needs for an autonomous monitoring system and to help them in decision-making. Within the framework of this study an enterprise model of orchard management is designed, which supports the digital twin concept and provides autonomous orchard monitoring. The study is scoped with subjects: apples, pears and cherries, and yield management based on orchard monitoring using UAV.