Nishant Kothari

Solar energy is rapidly being adopted across the world, and many photovoltaic (PV) systems are being installed. The increased demand for solar PVs has brought along the need for better inspection processes that can accurately detect defects like electrical and structural anomalies. Electrical and structural defects can cause solar PVs to operate at reduced power efficiency and lower lifetime expectancy. Inspection methods should maximize time efficiency and return accurate diagnoses; therefore, out of many viable techniques, electroluminescence (EL) imaging has proven to be effective in identifying solar PV anomalies. In this study, we provide an in-depth comparison of YOLO11 and YOLO26 object detection architectures for the task of detecting solar PV defects from EL images. We evaluate YOLO11 and YOLO26 across different scale models, namely nano (n), small (s), and medium (m). To analyze the influence of class imbalance, the experiments are conducted on a dataset comprising 12 defect classes and its subsequent reduced subsets generated by excluding defect categories with limited samples based on predefined instance thresholds, with all results reported as mean ± standard deviation over five independent runs. The experimental results demonstrate that defect detection performance improves as underrepresented classes are removed. YOLO26s achieved the highest performance with an mAP@50:95 of 0.6266 ± 0.0030 and an F1-score of 0.8337 ± 0.0013 on the 6-class subset. YOLO26n also exhibited competitive performance (mAP@50:95 of 0.6017) with only 2.38 M parameters, while YOLO11n required the shortest training time. These findings provide valuable insights into model selection for scalable and automated PV health monitoring systems.

An advanced IoT-based Liquefied Petroleum Gas (LPG) cylinder monitoring and safety system is presented in this work. The proposed technique provides continuous monitoring of residential gas usage and detects any potential leakage. It utilizes an MQ135 gas sensor for gas leakage detection, a load cell to monitor the weight of the cylinder, and a DHT22 sensor for temperature sensing. The sensors are mounted on a customized trolley for domestic LPG cylinders. All the sensors are connected to a NodeMCU microcontroller, which exchanges sensor data with a cloud platform using HTTP GET and POST methods to transmit the data to a cloud-based MySQL database. Unlike other existing methods, the proposed approach does not necessitate any modifications to the existing setup, which includes the gas cylinder, regulating valve, and distribution pipe. Furthermore, a mobile application that emphasizes the needs of the user is developed to enable a wider range of functionalities using cloud data collected from the sensors. The software facilitates the real-time monitoring of gas levels, provides comprehensive usage records for daily, weekly, and monthly intervals, issues immediate alarms in the event of gas leakage and low gas levels, and detects any unauthorized movement of the LPG cylinder, such as theft. The proposed technique not only improves user safety but also streamlines gas cylinder management with predictive analytics based on gas consumption trends and projected days of usage. Moreover, the application includes functionality that automatically orders a new cylinder with the vendor when the gas level drops below a predetermined threshold, therefore ensuring continuous availability of gas supply.