This work presents the design and prototyping of an Industrial Monitoring and Protection System aimed at enhancing safety and operational efficiency in industrial environments. The system integrates multiple sensors with a GSM module to monitor and respond to critical environmental parameters, such as ambient light levels, temperature, and smoke detection. A Light Dependent Resistor (LDR) is configured to detect excessive lighting levels, interfacing with a microcontroller to activate the GSM module and send alert messages when thresholds are exceeded. The temperature sensor continuously monitors ambient temperature, and upon detecting overheating, the microcontroller triggers the GSM module to notify operators. Similarly, a smoke sensor detects the presence of harmful smoke and initiates an alert through the GSM module for early fire hazard detection. These sensors are connected to the microcontroller via analog and digital input pins, with their outputs processed to enable condition-based responses. A relay switch, controlled by the microcontroller, automatically disconnects connected loads when safety thresholds are breached, preventing equipment damage and ensuring personnel safety. Real-time sensor readings and system status are displayed on an OLED screen, providing operators with comprehensive, up-to-date information on the monitored environment. The system dynamically responds to environmental conditions by triggering alerts and actions based on customizable safety thresholds for light intensity, temperature, and smoke levels. This integrated architecture ensures seamless communication between sensors, the microcontroller, and the GSM module, delivering real-time monitoring, automated protective mechanisms, and early warning capabilities. The proposed system demonstrates the feasibility of affordable and scalable solutions for industrial safety, offering immediate responses to hazardous conditions while minimizing downtime. Furthermore, its adaptable design allows for customization across different industrial environments, making it suitable for a wide range of applications.

Autonomous vehicle (AV) is a broad field in artificial intelligence which has seen monumental growth in the past decade and this had a significant impact in bridging the gap between the capability the intelligence of human and the efficiency of machines. With millions of people losing their lives, or have being a victim of road traffic accidents. There is a need to find a suitable algorithm for a navigation system in an autonomous vehicle with the purpose of help mitigate the traffic rule violation that most human drivers make that lead leads to traffic accidents. With both researchers and enthusiasts developing several algorithms for AVs, this field has been split into several modules which continually broaden the scope of AV’s technology. In this paper, we focus on the lane navigation which has an important part of the AV movement on the road. Here lane decision making is optimized by using deep learning techniques in creating a Neural Network model that focuses on generating steering commands by taking an image the road mapped out with lane markings. The navigation aid is a front-facing camera mounted and images from the camera are used to compute steering commands. The end to end learning scheme was developed by Nvidia cooperation to train a model to compute steering command from a front-facing camera. The model does not focus on detecting the lane but only generating the appropriate command for steering AVs’ on the road. This focus on one objective of the model helps in maximizing the potential of better accuracy in lane navigation of our AVs. The modeled car navigates through the designed lanes accurately with the level of intelligence the car shows in maneuvering through the lanes shows this method is more suitable in lane navigation.

The evolving information technology abridges the hardship in the daily life of consumers all over the world, hence the application of this knowledge in the irrigation field is necessary nowadays. The exponential growth of demand in food is due to the ever-evolving population of the world, thus it becomes necessary to expand the present area of cultivation. Considering the present situation of weather change due to global warming as a result of industrial activities, farming via irrigation is the reliable process of food production. Water remains the only source for survival for crop production, thus optimal management and proper use of water become pertinent with the ever-increasing land for irrigation. Arduino based automatic plant irrigation control system; provides a simple approach to automated irrigation. This work makes use of the GSM module for the notification of the user about the situation in the farm, this project aims to design and implement an automatic plant irrigation control system using Arduino and GSM module. In this proposed system, there are two main parts hardware and software units. Mechanical units which are the hardware unit comprises of instrumentation systems and watering irrigation systems. The equipment system is based on microcontroller, flow meter, moisture sensor, LCD, and GSM module. The software part comprises of C++ code, this is to enable the linkage between various modules. The main control of this system is the microcontroller unit that serves as the brain for coordinating control for various modules of the system, it synchronizes and operates the watering system and notifies the user about the condition of the field and watering section via GSM module. Implementation of this project will significantly help in a water-saving of about 30 – 50% as compared to the conventional watering system like the sprinkler, improve growth and discourage weeds because water will only be served to the needed area, simple method and timer-based system for automatic watering can be incorporated for efficiency.