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All Journal Journal of Computer Science Advancements Techno Agriculturae Studium of Research
Vong, Soneva
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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THE ROLE OF BLOCKCHAIN TECHNOLOGY IN FOOD SECURITY ASSURANCE IN SWEDEN Phanpheng, Nayla; Vong, Soneva; Keolavong, Manivone
Techno Agriculturae Studium of Research Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i4.2000

Abstract

The study discusses the role of blockchain technology in ensuring food safety in Sweden, with a focus on improving supply chain traceability and transparency. The research aims to identify the extent to which blockchain can improve food safety as well as overcome adoption constraints across companies of various sizes. A qualitative descriptive approach was used, with data collected through interviews and analysis of case studies from food companies. The results show that blockchain is able to improve operational efficiency and regulatory compliance, especially in large enterprises, while small companies face cost constraints and access to technology. The conclusion of the study confirms that blockchain can be a strategic solution for better food security, with the implication that regulatory support and incentives are needed to expand the adoption of this technology in small and medium-sized enterprises.
A COMPARATIVE STUDY OF GPS-GUIDED TRACTOR AUTOSTEER VS. TRADITIONAL SEEDING TECHNOLOGIES ON MAIZE YIELD AND FUEL EFFICIENCY Keolavong, Manivone; Vong, Soneva; Phommavong, Soukchinda
Techno Agriculturae Studium of Research Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i5.2959

Abstract

This study compares the impact of GPS-guided tractor autosteer technology and traditional manual steering on maize yield and fuel efficiency. Precision agriculture technologies, such as GPS-guided autosteer, offer more accurate and efficient field operations, reducing overlaps and gaps in seeding, which are common in manual methods. However, there is limited empirical evidence on the agronomic and operational performance of these technologies in maize cultivation. The research was conducted on maize farms over one growing season, with two treatments: GPS-guided autosteer and traditional manual steering. Data on maize yield, fuel consumption, seeding accuracy, and operational time were collected and analyzed. The results showed that GPS-guided autosteer significantly improved seeding accuracy, reducing overlaps and leading to a 12% increase in maize yield compared to traditional methods. Additionally, fuel consumption was reduced by 18% due to more efficient coverage and reduced operational time. The autosteer system also demonstrated improved consistency in row spacing and plant population. This study concludes that GPS-guided autosteer technology offers both agronomic and economic advantages, increasing maize productivity, enhancing fuel efficiency, and promoting more sustainable, cost-effective farming practices.
COMPUTING AT THE EDGE: THE ROLE OF NEUROMORPHIC CHIPS IN INTELLIGENT ROBOTICS Keolavong, Manivone; Vong, Soneva; Phoutthavong, Thipphavone
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.3331

Abstract

The deployment of autonomous mobile robots in resource-constrained environments is currently impeded by the excessive power consumption and latency bottlenecks of traditional Von Neumann architectures. This study investigates the efficacy of neuromorphic computing as a hardware solution for low-power, low-latency edge intelligence, specifically focusing on obstacle avoidance and navigational endurance. A quantitative comparative analysis was conducted benchmarking a Spiking Neural Network (SNN) based control architecture against standard embedded GPU solutions, utilizing event-based vision sensors to evaluate energy efficiency, inference latency, and task success rates. Empirical results demonstrate that the neuromorphic architecture achieved a twenty-fold reduction in power consumption (0.25 W) and sub-millisecond latency, significantly outperforming synchronous baselines while maintaining a 98.2% navigational success rate. The findings validate event-driven processing as a superior paradigm for edge robotics, offering a sustainable path toward "Green Robotics" with extended operational autonomy independent of cloud connectivity.
Co-Authors Keolavong, Manivone Phanpheng, Nayla Phommavong, Soukchinda Phoutthavong, Thipphavone