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 - 8 of 8
  • 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
    RISK ANALYSIS FOR APPLE ORCHARD SURVEY AND MONITORING USING UAV
    (2023)
    Lienīte Litavniece
    ;
    Kodors, Sergejs
    ;
    Juta Dekšne
    ;
    Zarembo, Imants 
    ;
    Gunārs Lācis
    ;
    Antons, Pacejs 
    Risk analysis is an integral part of modern business management because successful business largely depends on the effective implementation of risk analysis. Agriculture is an important sector in the national economy, therefore Industry 4.0 increasingly provides digital solutions in orchard management, which facilitate and simplify decision-making in daily tasks. Meanwhile, unmanned aerial vehicles are applied as the agriculture sector's main monitoring and data acquisition tool. However, this means that it is necessary to pay attention to risk analysis due to the process of managing the orchard, where not only a person and the mechanized equipment controlled by him, which moves on the ground but also flying automated equipment participates. The purpose of the article is to perform the risk analysis for the survey and monitoring of orchards for yield estimation using unmanned aerial vehicles by considering commercial apple orchards in Latvia. The main thing is that most risks are predictable, but planning is necessary to reduce the probability of their occurrence.  
  • Publication
    SIMULATION OF A SCHOOL CANTEEN TO UNDERSTAND MEAL DURATION IMPACT ON FOOD WASTE
    (2021)
    Kodors, Sergejs
    ;
    Zhukov, Vitaliy
    ;
    Zarembo, Imants 
    ;
    Litavniece, Lienīte
    ;
    Lonska, Jeļena 
    ;
    Zvaigzne, Anda
    A system simulation is a one of the approaches to understand business processes or to explain them to other people. It is an excellent decision making solution to provide data-driven conclusions based on system modelling and experiments. This paper proposes simulation results of a school canteen. The aim of the research was to investigate the relation between a food waste amount and meal time duration. The proposed simulation was based on business process analysis, business process modelling, a Monte Carlo method and expert knowledge. The frequency distributions were constructed based on children meal duration observation completed by their mothers. It is a magnificent citizen science solution to involve mothers in the research because they can additionally better understand their children meal preferences and habits. Therefore, a questionnaire for citizens was developed, which can be applied to collect statistical data for model accuracy improvement and extension.
    Scopus© Citations 1
  • Publication
    Pathfinding Algorithm Efficiency Analysis in 2D Grid
    (2013)
    Zarembo, Imants 
    ;
    Kodors, Sergejs
    The main goal of this paper is to collect information about pathfinding algorithms A*, BFS, Dijkstra's algorithm, HPA* and LPA*, and compare them on different criteria, including execution time and memory requirements. Work has two parts, the first being theoretical and the second practical. The theoretical part details the comparison of pathfinding algorithms. The practical part includes implementation of specific algorithms and series of experiments using algorithms implemented. Such factors as various size two dimensional grids and choice of heuristics were taken into account while conducting experiments.
    Scopus© Citations 5
  • 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
    Urban Objects Segmentation Using Edge Detection
    (2013)
    Kodors, Sergejs
    ;
    Zarembo, Imants 
    This manuscript describes urban objects segmentation using edge detection methods. The goal of this research was to compare an efficiency of edge detection methods for orthophoto and LiDAR data segmentation. The following edge detection methods were used: Sobel, Prewitt and Laplacian, with and without Gaussian kernel. The results have shown, that LiDAR data is better, because it does not contain shadows, which produce a noise.
    Scopus© Citations 3
  • Publication
    Apple Scab Detection in the Early Stage of Disease Using a Convolutional Neural Network
    (2022)
    Kodors, Sergejs
    ;
    Gunārs Lācis
    ;
    Inga Moročko-Bičevska
    ;
    Zarembo, Imants 
    ;
    Olga Sokolova
    ;
    Toms Bartulsons
    ;
    Apeināns, Ilmārs
    ;
    Vitālijs Žukovs
    Modern reviews of challenges related to deep learning application in agriculture mention restricted access to open datasets with high-resolution natural images taken in field conditions. Therefore, artificial intelligence solutions trained on these datasets containing low-resolution images and disease symptoms in the advanced stage are not suitable for early detection of plant diseases. The study aims to train a convolutional neural network for apple scab detection in an early stage of disease development. In this study a dataset was collected and used to develop a convolutional neural network based on the sliding-window method. The convolutional neural network was trained using the transfer-learning approach and MobileNetV2 architecture tuned on for embedded devices. The quality analysis in laboratory conditions showed the following accuracy results: F1 score 0.96 and Cohen’s kappa 0.94; and the occlusion maps — correct classification features.
    Scopus© Citations 1
  • 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.