Pratyush

Implementation of IoT-Based Smart Irrigation System for Agricultural Transformation(AIC2023). Our research presents an IOT-based irrigation method for agriculture. IoT is one of the new technologies of the twenty-first century. The agriculture industry is undergoing a transformation because of the Internet of Things (IoT), which is also fixing the tremendous problems or substantial challenges that farmers now face in the field. IoT and wireless connectivity might be helpful and time-saving for farmers. This paper offers a comprehensive review of existing IoT-based applications and research in the agricultural sector. While previous research papers have presented complex devices that can be challenging for farmers to operate and interpret, our proposed system focuses on simplicity and user-friendliness. This irrigation system is designed to be easily understood and used by farmers. The proposed system makes use of an Arduino-UNO, a Bluetooth module, and a soil-moisture sensor. By utilizing an Arduino UNO, our system enables real-time monitoring of various parameters, with a primary focus on soil moisture levels. Recognizing that different plant species have unique water requirements, our proposed system allows farmers to set customized threshold moisture levels for their crops. When the soil moisture falls below the defined threshold, the system activates the motor, initiating the irrigation process. By avoiding both overwatering and underwatering, this system contributes to improved agricultural practices. Link: https://ieeexplore.ieee.org/document/10263854/

Employing Machine Learning to Track Students’ Academic Progress and Learning Outcomes Across Schools. Our focus is on tracking students’ academic progress and learning outcomes in real-time across schools in this paper. In this work, This research will introduce a learning management system (LMS) based on desktop-based technologies and machine learning (ML). For a variety of tasks, including attendance tracking, assignment and quiz preparation, subject-specific mark analysis, and teacher and parent notification, the LMS has been created with a variety of ML algorithms. Lowering the amount of time spent on activities that a machine can perform, benefits both students and teachers.

Design and Implementation of an IoT-based AI Surveillance Car with Obstacle Detection, Line Following, and Live Streaming. The research paper presents the design and implementation of an AI Surveillance car that can perform object detection, line following, and live streaming. The proposed system is based on Internet of Things (IoT) technology, which allows the car to be remotely controlled and monitored. The car’s capabilities include real-time object detection, which enables it to detect and track objects in its surroundings, line following, which allows it to follow a predefined path, and live streaming, which provides remote access to the car’s camera feed. The results show that the proposed system can accurately detect obstacles and follow lines, while also providing high-quality live-streaming capabilities. Overall, the research provides a comprehensive and practical approach to designing an AI Surveillance car using IoT technology. Link: https://ieeexplore.ieee.org/document/10176395

Voice-Controlled Car Prototype: Advancing Human-Machine Interface with NLP and Wireless Communication. Our project presents a Voice-Controlled Car prototype aimed at filling the gap in systematic evaluations of voice-controlled systems. The prototype utilizes Natural Language Processing (NLP) techniques, and an Arduino UNO interfaced Bluetooth module for wireless communication with the “AMR Voice Control” Android app. Through algorithmic processes, the system extracts and executes multiple voice commands sequentially. Extensive testing with multiple phrases demonstrates strong performance in the Bluetooth range (8.5–12 meters) and response accuracy. The prototype is capable of extracting commands like forward, backward, stop, left and right form sentences. It follows all these commands one-by-one in a sequence. Additional features include live streaming via an ESP32-CAM module and obstacle recognition using an ultrasonic sensor, enhancing its practicality in real-world scenarios. This project offers an effective and practical voice-activated solution for Human-Machine Interface (HMI) applications, prioritizing usability and practicality. Link: https://www.publications.scrs.in/chapter/978-81-955020-8-0/1

Enhancing Efficiency and Functionality of Voice-Controlled Cars through NLP Techniques and Additional Features. This research introduces a Voice-Controlled Car prototype that addresses the existing literature gap in systematic evaluations of voice-controlled systems. The prototype employs Natural Language Processing (NLP) techniques and an Arduino UNO-interfaced Bluetooth module to facilitate wireless communication with a dedicated Android app, “AMR Voice Control.” Through an algorithmic process, the system extracts and executes multiple voice commands sequentially. Strong performance in terms of Bluetooth range (8.5–12 m). The effectiveness of the technique is demonstrated by the short processing times (2–7 ms) for command extraction and execution times ranging from 8.95 to 21.08 s. The prototype was tested with 50 statements and demonstrated solid performance. The average execution time for six commands takes 20.11 s. The prototype has extra features like live streaming via an ESP32-CAM module and obstacle recognition using an ultrasonic sensor to increase its usefulness in real-world scenarios. Performance study uses Python and data visualization tools to visualize the relationship between execution time and the number of instructions, which offers valuable insights for future voice-controlled system optimizations. This research provides an effective and practical voice-activated solution for HMI applications, with a focus on usability and practicality. Link: https://link.springer.com/chapter/10.1007/978-981-97-6588-1_10