Now showing 1 - 4 of 4
  • Publication
    Apple and Pear Scab Expert System
    (2023)
    Apeināns, Ilmārs
    ;
    ;
    Gunārs Lācis
    ;
    Lienīte Litavniece
    Plant disease, such as apple and pear scab, control is a crucial issue of fruit-growing. Apple and pear are among the most widely grown (approximately 43% of all fruit tree area) and economically important fruit crops worldwide and in Latvia. Research projects have produced research data covering various aspects of plant-pathogen interactions, but there is no internal linkage analysis, as well as implementation of other types of data (such as environmental and meteorological data, etc.). Establishing such a data integration system would allow the identification of new regularities in plant-pathogen interactions, and provide mechanisms for disease control decisions. In this study an expert system was developed aimed to help professional fruit-growers evaluate the possible impact of apple and pear scab to the plant health and yield quality. The expert system is based on a previously developed apple and pear scab ontology and consists of a web based front-end and triplestore back-end.
  • Publication
    RAPID PROTOTYPING OF PEAR DETECTION NEURAL NETWORK WITH YOLO ARCHITECTURE IN PHOTOGRAPHS
    (2023)
    Kodors, Sergejs
    ;
    Marks Sondors
    ;
    Gunārs Lācis
    ;
    Edgars Rubauskis
    ;
    Apeināns, Ilmārs
    ;
    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
    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.