Volume 14 | Issue 6 |

 Abstract

Air Quality Monitoring Using Drone: A Quadcopter-Based Real-Time Atmospheric Sensing System

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Air Quality Monitoring Using Drone: A Quadcopter-Based Real-Time Atmospheric Sensing System

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 Abstract

Modern agriculture requires efficient water management, especially in hilly regions where uneven terrain makes irrigation difficult. Traditional irrigation methods often result in improper water distribution, increased wastage, and reduced crop productivity. To overcome these challenges, this project presents an IoT-based smart irrigation system designed for multi-crop cultivation, specifically for Wheat and Jowar crops.The system uses NodeMCU modules as wireless sensor nodes to monitor soil moisture, temperature, and humidity in real time across different crop zones. The collected data is transmitted to the Blynk cloud platform via Wi-Fi, enabling continuous monitoring through a mobile application. A Raspberry Pi acts as the central controller, processing the data and controlling irrigation components such as a water pump and solenoid valves using a relay module.The system operates in both automatic and manual modes. In automatic mode, irrigation is controlled based on soil moisture thresholds to ensure optimal water usage. In manual mode, users can monitor data and control the system remotely through the Blynk mobile application.This system improves water efficiency, reduces manual effort, and ensures proper irrigation in hilly environments. It is cost-effective, scalable, and supports real-time agricultural applications, contributing to sustainable farming and improved crop yield.

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 Keywords

IoT, Smart Irrigation, Raspberry Pi, NodeMCU, Soil Moisture Sensor, Blynk, Automation

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Tampering in weighing and measuring instruments poses a significant threat to commercial accuracy, consumer protection, and fair-trade practices. With the increasing shift from mechanical to digital systems, new forms of tamperingsuch as sensor bypassing, data manipulation, magnetic interference, and microcontroller hacking have emerged. This project focuses on the development of an intelligent tamper-detection and prevention system designed to ensure the integrity and reliability of weighing instruments. The proposed system integrates multiple security mechanisms including load cell monitoring through HX711 amplifier modules, tamper switches for enclosure security, magnetic field detection, and anomaly-based detection algorithms within a microcontroller unit. Any detected irregularity triggers visual and audible alarms and can log the event for regulatory inspection. Additionally, protective circuit design, shielding techniques, and secure firmware architecture are employed to minimize vulnerabilities. The outcomes demonstrate that a multi-layered approach combining hardware, software, and mechanical safeguards significantly increases resistance to tampering and enhances accuracy and trustworthiness in measurement systems. This solution supports legal metrology standards and promotes transparency in commercial transactions.

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Tampering detection, Weighing instruments, Legal metrology, Load cell security, HX711 amplifier, Microcontroller-based monitoring

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 Abstract

AgroPredict: Intelligent Analysis of Soil Data for Crop Suggestion

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AgroPredict: Intelligent Analysis of Soil Data for Crop Suggestion

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The integration of automation technologies in the hospitality industry is becoming increasingly important for improving service quality and reducing operational costs. This paper presents the development of a cost-effective autonomous service robot designed for indoor hospitality environments such as hotels, restaurants, and office spaces. The system is specifically designed to be low-cost, reliable, and easy to deploy, making it suitable for small and medium-sized enterprises.

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Design and Implementation of a Low-Cost Autonomous Hospitality Robot for Smart Indoor Service Application

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 Abstract

India's 146 million smallholder farming households continue to rely heavily on diesel-powered tillers for soil cultivation, weeding, and trenching. These machines consume 1.2-1.5 litres of diesel per operating hour, generating roughly 3.4 kg of CO? per hour and imposing an operating fuel cost of INR108-135/hr on marginal farmers. This paper presents the fabrication, testing, and detailed energy analysis of a 48 V brushless DC (BLDC) motor-driven electric soil cultivator, developed by third-year B.Tech students of the Electrical Engineering department at R.C. Patel Institute of Technology (RCPIT), Shirpur, Maharashtra, as part of their 2025-26 departmental project.

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 Keywords

Electric cultivator, battery-powered weeder, BLDC motor, 48 V LiFePO?, FOC controller, Indian agriculture, energy saving, rural student fabrication, RCPIT Shirpur, JAP-O design.

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The rapid adoption of electric vehicles (EVs) worldwide has created an urgent need for sophisticated monitoring systems capable of tracking vehicle performance, battery health, energy consumption, and location in real time. This review paper examines the current state of real-time EV monitoring systems, exploring their architecture, key technologies, communication protocols, and applications. We analyse the challenges facing these systems and discuss emerging trends that will shape their future development. The integration of Internet of Things (IoT), cloud computing, machine learning, and advanced sensor technologies has enabled unprecedented capabilities in EV monitoring, [1] contributing to improved safety, efficiency, and user experience.

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Electric vehicles, real-time monitoring, battery management system, IoT, telematics, cloud computing, machine learning, fleet management

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This work presents an IoT-enabled hybrid renewable energy charging station designed for electric vehicle applications. The system integrates a 12V/10W polycrystalline solar photovoltaic panel and a 3V DC generator motor functioning as a small-scale Wind Energy Conversion System to address the intermittency challenges inherent in single-source renewable installations. The station employs an ESP32 DevKit V1 microcontroller with dual-core Xtensa 32-bit LX6 processor, integrated 12-bit analog-to-digital conversion capability, and built-in Wi-Fi connectivity.

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 Keywords

Hybrid renewable energy systems, wireless power transfer, Internet of Things, electric vehicle charging, ESP32 microcontroller, Blynk cloud platform.

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 Abstract

The rapid global integration of Photovoltaic (PV) systems requires robust monitoring and diagnostic mechanisms to ensure high efficiency and operational safety. PV systems are highly susceptible to various anomalies, including line-to-line faults, open circuits, partial shading, and degradation, which can severely diminish power output and cause permanent damage. Traditional fault detection methods rely on predefined thresholds, which often fail under fluctuating environmental conditions. This paper proposes a Machine Learning (ML) based approach for the automatic detection and diagnosis of faults in PV systems. By extracting key electrical features (voltage, current, power) and environmental data (irradiance, temperature), several supervised ML algorithms, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF), are evaluated. The methodology involves data preprocessing, feature extraction, and model training using simulated I-V characteristic curves under healthy and faulty conditions. The proposed model demonstrates high diagnostic accuracy, offering a reliable, sensor-efficient, and computationally viable solution for real-time predictive maintenance in solar energy systems.

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Photovoltaic System, Fault Diagnosis, Machine Learning, Artificial Neural Networks (ANN), Predictive Maintenance, Partial Shading.

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The rapid growth of photovoltaic (PV) solar systems has increased the need for efficient monitoring and maintenance techniques to ensure optimal performance. Faults such as hotspots, shading, cracks, and connection failures significantly degrade system efficiency. This paper presents a comprehensive performance analysis of PV systems using thermal imaging technology for anomaly detection. Infrared thermography enables non-contact, real-time monitoring of PV modules by detecting temperature variations associated with defects. The study analyzes various anomalies, evaluates their impact on system performance, and highlights the effectiveness of thermal imaging combined with image processing techniques.

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 Keywords

Photovoltaic systems, thermal imaging, anomaly detection, hotspots, infrared thermography, fault diagnosis.

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Creative Commons Attribution 4.0 and The Open Definition