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Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil and water, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent applications, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This paper investigates the transformation of traditional agriculture into smart farming through the integration of IoT technology and community partnerships. It presents a case study focused on educating farm owners about advanced technologies to enhance decision-making, improve crop yields, and promote sustainability. Additionally, the paper highlights the role of data analytics in agriculture. Farmers in the southern region of Zagreb, Croatia, were trained on the use of sensors and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial experience. The DMAIC methodology was employed to address these issues and measure relevant parameters. The paper also discusses consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It explains the potential of estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, Principal Component Analysis (PCA) biplot analysis is presented, showing that pH values behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices. [ABSTRACT FROM AUTHOR]

With the rapid growth of video data traffic on the Internet and the development of new types of video transmission systems, the need for ad hoc video encoders has also increased. One such case involves Unmanned Aerial Vehicles (UAVs), widely known as drones, which are used in drone races, search and rescue efforts, capturing panoramic views, and so on. In this paper, we provide an efficiency comparison of the two most popular video encoders—H.264 and H.265—in a drone piloting system using first-person view (FPV). In this system, a drone is used to capture video, which is then transmitted to FPV goggles in real time. We examine the compression efficiency of 4K drone footage by varying parameters such as Group of Pictures (GOP) size, Quantization Parameter (QP), and target bitrate. The quality of the compressed footage is determined using four objective video quality measures: PSNR, SSIM, VMAF, and BRISQUE. Apart from video quality, encoding time and encoding energy consumption are also compared. The research was performed using numerous nodes on a supercomputer. [ABSTRACT FROM AUTHOR]