Green Intelligent Systems and Applications

A Systematic Literature Review of YOLO and IoT Applications in Smart Waste Management

2025-07-11T07:04:40+00:00

The increase in urbanization and global population expansion resulted in increased garbage production, causing considerable environmental and public health issues that exceeded traditional waste management approaches. To tackle these challenges, automated waste detection and analysis integrated computer vision, especially deep learning, with the Internet of Things (IoT) in intelligent waste management applications. This comprehensive literature review investigated a wide range of You Only Look Once (YOLO) applications in IoT-based waste detection and management, demonstrating its efficacy in addressing global waste issues. Employing specific keywords and Boolean operators, the review followed a rigorous methodology to explore reputable electronic databases for peer-reviewed articles published from 2019 to 2025. The primary findings indicated that different iterations of YOLO (v3 to v12) were integrated with diverse IoT devices and computing setups, including edge and centralized systems. These integrations facilitated four crucial applications: hazardous waste management, monitoring of smart bins, classification of waste types, and detection of litter in public spaces. This integration enhanced sustainability through improved waste management practices, increased efficiency in waste processes, and reduced manual labor requirements. Challenges included precise waste identification in complex scenarios, adaptation to fluctuating environmental conditions, and ensuring dependable, low-power operation of IoT devices. To sum up, the integration of YOLO and IoT established a robust basis for intelligent waste management, transforming reactive approaches into proactive strategies. Moving forward, research should prioritize enhancing the integration and power management of IoT sensors, optimizing edge deployment, and developing more resilient YOLO models.

Design and Build a Push-Pull Inverter for Room Lighting

2025-08-03T01:31:46+00:00

This study addressed the issue of harmonic distortion in solar power systems that required inverters to convert DC voltage to AC for indoor lighting applications. The objective was to design and evaluate a push-pull inverter incorporating pulse width modulation (PWM) to reduce harmonics and ensure a stable voltage output. A push-pull topology was selected because of its relatively simple design and ability to step up DC voltage using a transformer, making it suitable for low- to medium-power applications. The inverter employed two metal–oxide–semiconductor field-effect transistor (MOSFET) switching devices operated alternately to generate AC waves at the output. The core of the design was a 50 Hz pulse generator producing a 5 V pulse signal with a small current, which was then amplified using a current amplifier before being supplied to the transformer. The transformer functioned to induce the electromagnetic field from the pulse source and release it at a higher voltage of 220 V. Experimental testing was performed using 2.3 W, 5 W, and 8 W LED lights. A minor modification to the gate resistor improved system performance, resulting in stable transformer output voltages at 5 W and 8 W loads. These results demonstrated that the PWM-controlled push-pull inverter successfully reduced harmonics and maintained voltage stability under higher loads, making it effective for indoor LED lighting powered by solar energy. Future studies could aim to enhance efficiency at lower loads, minimize switching losses, and implement more advanced PWM techniques to achieve performance levels comparable to pure sine wave inverters.

Implementation of Key Performance Indicators in the Palm Oil Harvest Monitoring Information System

2025-08-07T01:37:04+00:00

Manual management of palm oil harvest data often resulted in data inconsistencies, low operational efficiency, and financial losses for plantation companies. To address these challenges, this study aimed to implement the Key Performance Indicator (KPI) method within a harvest monitoring information system at PT Perkebunan Nusantara (PTPN; Nusantara Plantation Company, Ltd) IV Regional II Unit Adolina, with the goal of enhancing data accuracy, transparency, and managerial effectiveness. A quantitative research approach was employed, utilizing data collection techniques such as observation, interviews, and document analysis. Five out of ten afdeling units were selected as research samples. The performance was assessed using three primary indicators: total harvest yield (weighted at 40%), labour productivity (35%), and monthly harvest frequency (25%). The results revealed significant variations in KPI achievement across afdeling units, with several units exceeding the established targets. Furthermore, the developed system featured an interactive visual dashboard that enabled managers to monitor performance in real time and supported data-driven decision-making. Compared to traditional monitoring tools, this system offered enhanced integration of performance metrics, automated data processing, and real-time analytics, addressing previous limitations such as delayed reporting and fragmented data sources. In conclusion, the integration of KPI into the harvest monitoring information system proved to be effective in providing objective and measurable performance evaluation. This approach offered a strategic solution for improving operational efficiency and productivity in palm oil plantation management.