Raspberry Pi Research Articles

Car Washing System using raspberry Pi Pico as PLC

Abstract: Using the Raspberry Pi Pico microcontroller, we have designed and implemented an automatic vehicle wash system in this work. By automating steps including scrubbing, rinsing, drying, and applying soap, the system seeks to expedite the automobile washing process. We go over the Raspberry Pi Pico, actuators, and sensor hardware configuration in addition to the software architecture that controls the system. We also emphasize the advantages of utilizing Raspberry Pi Pico for this application, such as its affordability, portability, and versatility. The efficacy and dependability of the suggested automatic car wash system are shown by the experimental results.

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

A Deep Learning Based Framework for Accurate Prediction of Disease of Crops and Nutrients in Smart Agriculture

Abstract: The health and production of crops have a major impact on societal well-being, and agriculture is essential to maintaining global food security. Crop output and quality are greatly impacted by nutrient shortages and crop diseases. We offer a unique approach to this problem that makes use of the Raspberry Pi, an inexpensive, small, and energy-efficient single-board computer, to forecast crop illnesses and identify nutrient deficits in real time. Our solution provides farmers and other agricultural stakeholders with fast and accurate information for efficient crop management by combining computer vision, machine learning, and sensor technologies. The Raspberry Pi has many sensors to assess ambient and soil conditions in addition to a high-resolution camera for taking pictures. Using a machine learning model trained on a variety of datasets pertaining to agricultural illnesses and nutritional deficits, the system takes pictures of crop leaves and soil conditions. Preprocessing images, feature extraction, and convolutional neural network (CNN)-based categorization are important parts of our approach. The system can recognize evidence of nutrient deficits, such as nitrogen, phosphorus, and other deficiencies, as well as symptoms of a variety of crop illnesses, including fungal infections, bacterial blights, and viral diseases.

Automated Water Meter Reading through Image Recognition

Abstract: In this paper, we propose a solution for reading an analog water meter automatically by digitalizing the meter. Since digital water meters are expensive, we retrofit the meter using Raspberry Pi, a camera, and an LED. Though the structure of the analog water meter is simple, consumes less power, and has high durability, it has some disadvantages. Primarily, it requires human effort for reading, which is time-consuming and causes inconvenience to users. To address this challenge, there is a need for an automated system. Thus, the project is implemented using image recognition techniques, specifically Optical Character Recognition (OCR), and Internet of Things (IoT). This method accurately detects the numerical values present on the meter, and this data is transmitted to ThingSpeak, an IoT platform. Overall, this project offers an effective solution for reading water meters and enhance the comfort and convenience of users.

Autonomous Road way Alert System

The "Autonomous Roadway Alert System" design process, which is based on the Waterfall Model for the System Development Life Cycle (SDLC), is discussed in this article. These sign board detection, traffic sign detection, obstacle detection, lane assistant and voice assistant features are the functions of the system tackled through phases of collection and analysis, design, implementation, integration, testing, deployment, and maintenance processes. Emphasis of use of Python-coded programs on Raspberry Pi hardware and YOLOv5 algorithm is given for the purpose of object identification.

AI-Powered Disease Detection and Growth Monitoring System Using IoT for Improved Tomato Yield

Abstract: This project introduces an advanced AI- powered disease detection and growth monitoring system designed specifically for enhancing tomato yield. With Internet of Things (IoT) technology, the system integrates Raspberry Pi as the central processing unit with temperature and humidity sensors for environmental monitoring. AI algorithms are used to predict tomato ripening stages and detect diseases, to ensure proactive management of plant health. If a disease is identified, the system provides the solutions to control its impact, thereby enhancing yield and quality. The captured data is analysed and results are displayed on a user-friendly webpage accessible to farmers, enabling informed decision-making and improved agricultural practices. This innovative approach combines technology with agricultural expertise, offering a solution to the challenges faced by tomato farmers and contributing to sustainable tomato production. Keywords: Image Recognition, Disease Detection

Smart Automated Irrigation System using Wireless Technology

Abstract: The need for agricultural output has surged due to the exponential rise of the world population, resulting in a strain on water supplies and a need for more effective irrigation systems. The goal of this project is to optimize water utilization in agricultural settings by presenting an automated and intelligent irrigation system. Based on current soil moisture levels, the system monitors and regulates irrigation using soil moisture sensors, an Arduino Nano, and a Raspberry Pi 3B+. By converting analog signals from the soil moisture sensor into digital data, the Arduino Nano serves as an analog-to-digital converter. This digital data is sent to the Raspberry Pi 3B+, which acts as the central control unit. A MOSFET is then triggered to activate a relay that is coupled to a 5V DC water pump when the moisture levels drop below a certain threshold. The system also has wireless connection, which enables remote control and monitoring via an Android application. Users have the ability to manually override the automatic system if needed, monitor the moisture content of the soil, and turn on or off the water pump. This feature improves the usability and flexibility of the system.

Design and Development of IIOT Based Prototype Model by Using Machine Learning Techniques for Workflow Management in Industry 4.0

Manufacturing is undergoing a revolutionary shift known as Industry 4.0 (I4.0), which unifies production and business operations through data-driven End-to-End digital integration. I4.0 goes beyond the digital ecosystem of the Third Industrial Revolution to include autonomous tech-human convergence and real-time data interchange. The Industrial Internet of Things (IIoT) and machine learning are driving breakthroughs in cyber-physical systems, which form the basis of this. In order to advance I4.0, technology and human talents must be balanced. It is essential to upskill the workforce and engage in coordinated transdisciplinary activities. A holistic approach, integrating human factors and tech advancements, ensures smooth automation transition. The proposed study introduces a cost-effective liquid filling system based on a Raspberry Pi, addressing automation challenges for smaller firms.

Determining the Quality of Measuring the Level of Lower Extremity Joint Movement in Inclusive Physical Education Using Electronic IT Resources

Objectives. The study aimed to investigate the quality characteristics of the tool developed based on electronic IT resources for measuring the level of movements in the joints of the lower limbs of students with disabilities caused by injuries. Material and methods. The experimental study involved 32 first-year students who had sustained lower limb injuries as a result of the war and were in remission. The methods used included analysis, synthesis, systematization, generalization, technical modeling, pedagogical experiments, and mathematical statistics. Results. A means of measuring movements in the joints of the lower limbs has been developed using electronic IT resources. The basis of the tool is a measuring line consisting of a printed circuit board on which is placed a design of capacitive sensors, a switching line, and a signal converter that measures the signal received by the sensors. The measurement results are transmitted to the controller and then to the PC via an interface implemented based on Bluetooth wireless technology. To implement the measuring tool, a controller is used, which has a board built on the synthesis of the Arduino electronic hardware platform and the Raspberry Pi minicomputer. The test results are displayed on the PC monitor screen. The determination of the qualitative characteristics of the test, in the case of recording the results of a tool developed on the basis of electronic IT resources for measuring the level of movements in the joints of the lower limbs of students with disabilities, established that the level of reliability of the test is above 0.90 (“excellent”) validity – from 0, 6 (“high”). Empirical data collected directly from the experiment have a low level of reliability: qualitative indicators of reliability in the range are below 0.70 (“may have limited applicability”), validity – < 0.3 (“low validity”). Conclusions. Developed based on electronic IT resources, the tool for measuring the level of movements in the joints of the lower limbs of students with disabilities due to limb injuries has significant advantages, such as the availability of functions, ease of use and efficiency. Ensuring high efficiency and objectivity of control contributes to performing control operations in real-time. By using assessment tools with a high level of reliability and validity, we ensure the detection of reliable changes in the state of the joints of the lower limbs of students with disabilities, thus eliminating the influence of errors in making managerial decisions in the planning process of their PE.

Traffix : Efficient Traffic Control using IoT

This paper aims to alleviate traffic congestion brought on by antiquated, ineffective traffic management systems that are based on a predefined countdown. Long red light delays are the result of these traditional systems, which have a predefined countdown regardless of the actual traffic on a given road. Our system makes sure that time set for the traffic lights reflects the traffic density in real time, which ensures efficient use of time. In order to do this, we first compute the traffic density, which is ascertained by combining image processing methods along with the use of ultrasonic sensors. The Raspberry Pi then processes this data and then manages the time set for the traffic light. Furthermore, the use of SQL Alchemy ensures that this processed information is stored to the cloud, where it may be utilized in events of sensor failure or system malfunction. Index Terms—Image Processing, Raspberry Pi, Traffic Congestion, Ultrasonic sensors

Low-cost Raspberry Pi star sensor for small satellites. II: StarberrySense flight and in-orbit performance

Low-cost Raspberry Pi star sensor for small satellites. II: StarberrySense flight and in-orbit performance

IoT Based Smart Trolley for Auto Billing

This paper proposes the development of a Smart Trolley system aimed at enhancing the shopping experience by automating the billing process. The system integrates Raspberry Pi, a low-cost, credit-card-sized computer, and RFID (Radio-Frequency Identification) technology to create an efficient and seamless checkout process for customers in retail environments. The Smart Trolley employs RFID tags attached to products and an RFID reader mounted on the trolley to identify items as they are placed inside. The Raspberry Pi processes the RFID data and maintains a running total of the items in the trolley. Additionally, it communicates with the store's database to retrieve product information and pricing. Through this integration, the Smart Trolley eliminates the need for manual scanning of each item at checkout, reducing waiting times and enhancing overall efficiency. Moreover, the system provides real-time updates on the total bill, allowing customers to track their expenses as they shop. This paper outlines the design, implementation, and testing of the Smart Trolley system, demonstrating its effectiveness in automating the billing process while improving the shopping experience for customers. The proposed system holds promise for revolutionizing retail operations, offering a glimpse into the future of smart, technology-driven shopping solutions. Key Words: Autobilling, Rasberry Pi, RFID(EM-18), LCD, Smart Shopping, IoT.

DETECTION OF ILLEGAL ON-STREET PARKING

The increase of automobiles in this generation is very high compared to the olden days. Due to the increase of such automobile, there is also a gradual increase in terms of traffic in the countries. We wait for a long time based on the traffic in the cities. The main cause for some traffic to occur is due illegal parking on roads and other areas. In order to beat this drawback, we tend to propose a way to avoid traffic that is by detecting illegal on-street parked vehicles by the use of their number plates by image processing. On-street parking refers to practice of parking vehicle on the side of public road or street. The on-street parking is common method of parking in urban and suburban areas where parking lots or dedicated off-street parking space may be limited. Due to illegal on-street parking there will be traffic congestion and also accidents may occur. “Detection of Illegal On-Street Parking” provides data of the illegally parked vehicle by capturing the image of number plate and that image is converted to greyscale, segmented and masked in the pre-processing stage. Then using Computer Vision technique, bounding boxes are formed around characters and characters are also printed with the use of CV library. The proposed also generates the chalan or invoice of penalty stating the duration of time parked in. Key Words: on-street parking, area of interest, Raspberry Pi, image processing, Resnet, Yolo v3 model, OCR.

Real-Time Multiple Object Detection Using Raspberry Pi and Tiny-ML Approach

Introduction:: Object detection has been an essential task in computer vision for decades, and modern developments in computer vision and deep learning have greatly increased the accuracy of detecting systems. However, the high computational requirements of deep learningbased object detection algorithms limit their applicability to resource-constrained systems, such as embedded devices. Method:: With the advent of Tiny Machine Learning (TinyML) devices, such as Raspberry Pi, it has become possible to deploy object detection systems on small, low-power devices. Due to their accessibility and cost, Tiny-ML devices, such as Raspberry Pi, a single-board tiny-ML device that is extremely well-liked, have recently attracted a lot of attention. Result:: In this study, we present an enhanced SSD-based object detection approach and deploy the model using a tinyML device, i.e., Raspberry Pi. Conclusion:: The proposed object detection model is lightweight and built utilizing Mobilenet- V2 as the underlying foundation.

Recognition and Picking of Target Fruits of Crop Picking Vehicles based on Computer Vision

A crop-picking vehicle was designed to automatically pick crop fruits and reduce human labor in the later harvest. The intelligent vehicle uses a Raspberry Pi 3B + controller, combines with a monocular camera, infrared sensor, four-degree-of-freedom manipulator and manipulator, and adopts the open source computer vision library OpenCV to realize the recognition and extraction of the target fruit. Based on the actual test verification, it was concluded that the intelligent vehicle could successfully realize the picking test of simulation citrus under the condition of good ambient light.

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment.

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.

Child face detection on front passenger seat through deep learning

Objective One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat. Methods The system incorporates processing of data collected, elements of deep learning such as transfer learning, fine-tunning and facial detection to identify the presence of children in a robust way, which was achieved by training with a dataset generated from scratch for this specific purpose. The MobileNetV2 architecture was used based on the good performance shown when compared with the Inception architecture for this task; and its low computational cost, which facilitates implementing the final model on a Raspberry Pi 4B. Results The resulting image dataset consisted of 102 empty seats, 71 children (0-13 years), and 96 adults (14-75 years). From the data augmentation, there were 2,496 images for adults and 2,310 for children. The classification of faces without sliding window gave a result of 98% accuracy and 100% precision. Finally, using the proposed methodology, it was possible to detect children in the front passenger seat in real time, with a delay of 1 s per decision and sliding window criterion, reaching an accuracy of 100%. Conclusions Although our 100% accuracy in an experimental environment is somewhat idealized in that the sensor was not blocked by direct sunlight, nor was it partially or completely covered by dirt or other debris common in vehicles transporting children. The present study showed that is possible the implementation of a robust noninvasive classification system made on Raspberry Pi 4 Model B in any automobile for the detection of a child in the front seat through deep learning methods such as Deep CNN.

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions.

OCR Based Facilitator for the Visually Challenged

Your proposal for an OCR-based smart book reader catering to the visually challenged fills a crucial need for affordable accessibility solutions, particularly given the global population of approximately 285 million visually impaired individuals, with a majority residing in developing countries. Leveraging a Raspberry Pi 3 board and Tesseract OCR technology, your device converts printed or handwritten text into machine-encoded text, enhanced by Computer Vision libraries like OpenCV for image preprocessing. Beyond text conversion, features such as obstacle detection via a smart stick and an emergency help button exhibit a comprehensive approach to addressing user needs. Integration with an Android app for location tracking adds an extra layer of safety. Challenges in optimizing OCR for handwritten text and potential expansions like language translation or cloud integration hold promise for further enhancing functionality. Overall, your project stands to significantly enhance access to information and safety for visually impaired individuals, particularly in underserved communities. Key Words: OCR-based smart book reader, Visually Challenged accessibility, Raspberry Pi integration, Tesseract OCR technology Computer vision

Advancing Electoral Processes: A Novel E-Voting Model Integrating Iris Scans, Raspberry Pi, and Blockchain Technology

The amalgamation of an e-voting system with blockchain technology represents a significant advancement in the electoral process, streamlining voting procedures. However, concerns persist regarding the security and auditability of the system. While blockchain provides robust security and transparency to safeguard the integrity of votes, it faces potential resistance from tech-agnostic users. In response to these challenges, a novel e-voting model is proposed, leveraging iris scans through Raspberry Pi technology, offering a promising avenue for enhanced security and user acceptance. Moreover, this biometric approach introduces a seamless and user-friendly authentication process, potentially mitigating the resistance faced by tech-agnostic voters in adopting advanced e-voting technologies. The integration of iris scans through Raspberry Pi into the e-voting system, coupled with blockchain technology, presents a comprehensive and forward-thinking solution to the challenges posed by security, auditability, and user acceptance. This multifaceted approach not only addresses existing concerns but also sets the stage for a more inclusive and secure electoral process in the digital age.