Browsing by Author "Kupsch, Stephan"

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    Apiculturists’ issues and challenges:
    (IOP Conference Series: Materials Science and Engineering, 2019) Kupsch, Stephan; Palaoag, Thelma D.; Balcita, Amy
    The most significant activity of honey bees, as far as advantages to people, is their pollination of natural vegetation. Bees and other pollinators seem to be declining globally. Beekeeping in the Philippines is a thriving industry that perfectly matches the natural landscape of the country. The goal of this study was to define the challenges and issues of Apiculturists in the Philippines where-in an IT approach was formulated to address these issues. Up to now, there is no initiative Information Technology approach in the beekeeping industry in the Philippines. Design thinking has been used as methodology in this study. Several challenges and issues were determined, and it is stated that the main problem to be addressed in order for the bee colonies to have a longer lifespan is its Apiary Location, an IT solution via Drone Technology with a Computer Vision approach was proposed named as Apiary Locator. A proposed framework of the Apiary Locator has been prepared. It is then recommended for the Apiary Locator to be developed as it shall benefit the apiculturists of the Philippines. Also, it will be a basis for developing IT solutions for the Beekeeping industry.
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    Smart beekeeping: Machine learning for healthy hive location analysis
    (Don Mariano Marcos Memorial State University – Mid La Union Campus, 2026) Kupsch, Stephan
    This paper presents HaBEEtat WebApp, machine learning-driven application designed to enhance decision-making in apiculture by automating the identification of suitable hive locations. Utilizing drone-captured imagery and lightweight scripting through Tracking.js (a 7KB JavaScript computer vision library), the system analyzes environmental features such as sunlight exposure, vegetation density, and windbreaks to determine areas conducive to healthy hive development. The web-based platform embodies the principles of smart farming and precision agriculture by integrating image processing, artificial intelligence, and geospatial analysis for practical field applications. The performance and usability of HaBEEtat WebApp were evaluated using ISO/IEC 25010 software quality model, covering eight key quality attributes: functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. Results revealed high acceptability, with median ratings ranging from 4.0 (Agree) to 5.0 (Strongly Agree) across most categories, demonstrating the web app's reliability, interoperability, and efficiency in real-world agricultural settings. Findings confirm that lightweight machine learning architectures can effectively support small-scale and remote beekeepers, providing a low-resource yet powerful decision-support tool. The study concludes that HaBEEtat WebApp contributes to sustainable apiculture practices, aligns with digital transformation initiatives in agriculture, and offers a replicable model for other smart farming applications. Future work recommends developing a mobile-integrated version for real-time drone-assisted operations and wider accessibility among local apiculturists.