Now showing 1 - 3 of 3
  • Publication
    RISK ANALYSIS FOR APPLE ORCHARD SURVEY AND MONITORING USING UAV
    (2023)
    Lienīte Litavniece
    ;
    Kodors, Sergejs
    ;
    Juta Dekšne
    ;
    ;
    Gunārs Lācis
    ;
    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
    Apple Scab Detection in the Early Stage of Disease Using a Convolutional Neural Network
    (2022)
    Kodors, Sergejs
    ;
    Gunārs Lācis
    ;
    Inga Moročko-Bičevska
    ;
    ;
    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) ;
    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.