http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/658 2026-06-15T23:09:52Z 2026-06-15T23:09:52Z Riyad, Md. Shafayet Jamil Tahmid, Khandaker Ahanaf http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17310 2026-06-13T21:01:03Z 2025-10-25T00:00:00Z

IOT Base Soil Nutrient and Fertilizer Monitoring System For Smart Agriculture Riyad, Md. Shafayet Jamil; Tahmid, Khandaker Ahanaf In smart agriculture, accurate monitoring of soil is necessary for the maximization of crop yield and sustainable farming. In this project an Iot Base Soil Nutrient and Fertilizer Monitoring System for Smart Agriculture that can monitor soil remotely on a real time basis is proposed which works on ESP32 microcontroller. The system includes a 3-in-1 NPK (Nitrogen, Phosperous, Potassium), DS18B20 temp sensor and capacitive soil moisture sensor. These sensors acquire environmental parameters, that showed on a 0.96-inch OLED display and also sent at the same time to mobile application designed with MIT App Inventor using MQTT protocol. The RS485 bus is used for communication between ESP32 and the NPK sensor, MP1584 buck converter steps down 12V switching adapter to a safe 5V work voltage. I use the MQTT broker named broker. emqx. io, as it supports low latency light communication for IoT data. The product enables data driven predictive farming, controlling the environmental factors that affects yield and crop quality of yields. Scalable, less expensive and an effective technique for monitoring the agricultural field in real time can provide insightful information to farmers to improve productivity and resource utilization. Project Report

2025-10-25T00:00:00Z Shanto, Mushfiqur Rahman Sakib, Muktadir Khan Morshed, Nishat http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17309 2026-06-13T21:01:00Z 2025-08-30T00:00:00Z

Implementing Blockchain for Enhancing Cloud Security Shanto, Mushfiqur Rahman; Sakib, Muktadir Khan; Morshed, Nishat As digital data continues to grow rapidly across sectors, ensuring its integrity and authenticity presents a critical challenge—particularly in centralized cloud storage systems, which are vulnerable to unauthorized access, data tampering, and single points of failure. This project proposes a decentralized architecture that integrates Ethereum smart contracts, the InterPlanetary File System (IPFS), and a Flask-based backend to enhance file security, transparency, and traceability. By decentralizing file storage and verification, the system addresses key risks inherent in traditional cloud platforms. Each file is hashed into a unique Content Identifier (CID) via IPFS, and this CID is stored immutably using Ethereum smart contracts. Flask facilitates all backend operations between users, IPFS, and the blockchain, while Twilio is utilized for real-time WhatsApp alerts upon unauthorized access or CID mismatches. The system was rigorously tested using Ganache, Truffle, and Postman. Results demonstrated precise detection of file tampering, instant alerting, and a high level of reliability. The proposed model proves to be scalable, secure, and suitable for sensitive applications in legal, healthcare, and government data management. Project Report

2025-08-30T00:00:00Z Labib, Farhan Akter, Mahfuza http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14169 2025-09-02T21:01:58Z 2024-02-05T00:00:00Z

Design And Implemantation Iot Based Smart Village Farming With Sun Detecting Solar Powered Renewable Energy. Labib, Farhan; Akter, Mahfuza In order to implement effective and sustainable farming methods, this project suggests developing a smart farming system that makes use of cutting-edge sensor technology and Internet of Things integration. In this project, a PIR motion sensor, an infrared sensor, a rain detection sensor, a soil moisture sensor, and a humidity sensor have been used. For the actuator, a servo motor has been used to control irrigation valves and gates. For the central control system, an ESP8266 microcontroller with Wi-Fi has been used for data collection by mobile app communication, which is powered by solar panels for sustainable operation where sun-detecting solar panels capture renewable energy. In this project, the PIR motion sensor has detected and notified animals, or trespassers. An infrared sensor has been used for automated watering and feeding based on distance and soil moisture, which is useful for monitoring crop and animal positions. A rain detection sensor has been used to measure rainfall data, alert the farmer about impending flooding, and automate the irrigation system based on rain data. A soil moisture sensor has been used to determine soil moisture content, which enables automatic irrigation or notifies the farmer based on soil conditions, and a humidity sensor has also been used to measure air humidity, which gives real-time monitoring and actuator control via a mobile app. This project has been tested several times and has successfully achieved the desired output. This Internet of Things (IoT)-based Smart Village Farming system presents a viable means of advancing sustainability, updating agricultural methods, and providing farmers with data-driven decision-making resources. Project

2024-02-05T00:00:00Z Sanji, Tabassum Barka Roy, Vaskor http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/14168 2025-09-02T21:01:57Z 2024-01-31T00:00:00Z

Development of Intelligent Traffic Management System Sanji, Tabassum Barka; Roy, Vaskor Traffic management is a major challenge in densely populated countries like Bangladesh, where traffic violations are common and hard to enforce. In this project, we propose an Development of Intelligent Traffic Management System (DITMS) that uses computer vision techniques to automatically detect and record traffic infractions. The DITMS consists of four modules: (1) License Plate Detection using YOLOv8, a deep learning model that can identify and extract license plate numbers from images; (2) Speed Measurement with Kalman Filter, a mathematical method that can estimate the speed of vehicles from consecutive frames; (3) Vehicle Type Detection utilizing a Convolutional Neural Network (CNN), a machine learning model that can classify vehicles into different categories based on their shape and size; and (4) Vehicle Counting through a combination of object detection, tracking, and segmentation algorithms, which can count the number of vehicles passing through a given area. The DITMS can be deployed on roadside cameras or drones to monitor traffic flow and capture evidence of violations such as speeding, overloading, or illegal parking. The DITMS aims to digitize traffic infractions in Bangladesh and contribute to the vision of SMART Bangladesh 2041, a national initiative that seeks to transform the country into a digital and developed nation Project

2024-01-31T00:00:00Z