Raspberry Pi Microcontroller Research Articles

Evaluation of Smart Greenhouse Monitoring System using Raspberry-Pi Microcontroller for the Production of Tomato Crop

To provide fresh and highly nutritive food, automated greenhouses and smart farming systems had proved to be helpful for growing world population. The smart greenhouse house monitoring system not only helpful in exploiting the production but also helpful to bridge up the quality of the produce. The current study was designed to explore the potential use of a smart greenhouse monitoring system using Raspberry-Pi microcontroller. The aim of the study was to create a smart automation system to control the microclimate of greenhouse. Two varieties of tomato (Roma and cherry tomato) were used both in smart greenhouse system as well as in conventional greenhouse system to compare the agronomic and quality parameters. Temperature and humidity were set according to the production technology of tomato using automation system. Proper fertilization and irrigation requirements were considered equal in all aspects in smart greenhouse and conventional greenhouse. The smart greenhouse monitoring system worked better in maintaining the microclimate inside the greenhouse with a difference of about 5-6 ℃ temperature and 20-30% humidity higher than the conventional greenhouse. The results predicted a progressive increase in agronomic parameter with a difference of 10-15% in plant height, number of leaves, number of fruits and weight of fruit as compared with growth parameters in conventional greenhouse. Similarly the quality parameters were effective with maximum size of fruit in Roma variety that was 75 mm as compared to fruit size 65 mm in conventional farming. The over average yield of tomato per plant (5.5 kg/plant) was also recorded in smart greenhouse that was significantly increased as compared with conventional greenhouse. The results predicted that the yield of tomato was positively affected using smart greenhouse monitoring system and consequently, the smart technologies could be used for the potential crop production and monitoring of cultivation activities.

Handheld device quantifies breath acetone for real-life metabolic health monitoring.

Non-invasive breath analysis with mobile health devices bears tremendous potential to guide therapeutic treatment and personalize lifestyle changes. Of particular interest is the breath volatile acetone, a biomarker for fat burning, that could help in understanding and treating metabolic diseases. Here, we report a hand-held (6 × 10 × 19.5 cm3), light-weight (490 g), and simple device for rapid acetone detection in breath. It comprises a tailor-made end-tidal breath sampling unit, connected to a sensor and a pump for on-demand breath sampling, all operated using a Raspberry Pi microcontroller connected with a HDMI touchscreen. Accurate acetone detection is enabled by introducing a catalytic filter and a separation column, which remove and separate undesired interferents from acetone upstream of the sensor. This way, acetone is detected selectively even in complex gas mixtures containing highly concentrated interferents. This device accurately tracks breath acetone concentrations in the exhaled breath of five volunteers during a ketogenic diet, being as high as 26.3 ppm. Most importantly, it can differentiate small acetone changes during a baseline visit as well as before and after an exercise stimulus, being as low as 0.5 ppm. It is stable for at least four months (122 days), and features excellent bias and precision of 0.03 and 0.6 ppm at concentrations below 5 ppm, as validated by proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS). Hence, this detector is highly promising for simple-in-use, non-invasive, and routine monitoring of acetone to guide therapeutic treatment and track lifestyle changes.

Density Based Traffic Management System for Smart Cities

In most cities throughout the world, traffic congestion is a serious issue that has turned into a nightmare for the locals. It is brought on by signal delay, improper traffic signal timing, etc. Traffic does not affect the hard-coded traffic light delay, which is independent of it. Consequently, there is an increasing need for systematic quick automatic systems to optimise traffic control. This essay is intended to Upon detecting the level of traffic at the intersection, the signal time adjusts automatically. This work makes use of a Raspberry Pi microcontroller. Modern technology is being used to optimise traffic flow in cities, including the density-based traffic management system for smart cities. This technology uses a variety of sensors and cameras to gather real-time traffic information that is then analysed to spot congested regions and forecast future traffic trends. This data is used by the system to dynamically change traffic signal timings, redirect traffic to less crowded routes, and give drivers and pedestrians real-time information via mobile apps and electronic signage. This system offers a more effective, safe, and sustainable solution to managing urban traffic by utilising cutting-edge technologies like artificial intelligence, machine learning, and the Internet of Things (IoT). This system decreases travel times and carbon emissions while enhancing the quality of life for city dwellers.

Development of a semi-automated volatile organic compounds (VOCs) sampling system for field asymmetric ion mobility spectrometry (FAIMS) analysis

In recent years, applications of volatile organic compounds (VOCs) sensing technologies such as field asymmetric-waveform ion-mobility spectrometry (FAIMS) system in agriculture have accelerated. FAIMS system for VOCs sensing is attractive as it offers high sensitivity, selectivity, real-time monitoring, and portability. However, the development of a robust instrumentation system is needed for precise sampling, high accumulation of VOCs, and careful handling of samples. In this study, we developed a simple semi-automated VOC sampling (SAVS) system using a Raspberry Pi microcontroller, flowmeters, electromechanical solenoid, and cellphone-based app to control cleaning and sampling loops. The system was compared with customized headspace sampling apparatus (CHSA) and validated with a biomarker (acetone) identified to be associated with potato rot development during postharvest storage. The standard error within ion current data across different compensation voltage was lower using the SAVS system than the CHSA. In addition, the maximum peak values across scans displayed a high coefficient of variation using the CHSA (16.23%) than the SAVS system (4.51%). Future work will involve improving system efficiency by adapting multiple sample units, system miniaturization, and automating the flowmeter operation. Such automation is critical to characterize VOCs precisely and automatically across several samples for multiple applications such as pathogen detection, evaluation of crop responses, etc.

Development of an autonomous vision sensor-actuator-based circumferential seam path tracker welding machine/device for LPG cylinders

This paper presents an autonomous vision sensor-actuator-based circumferential seam path tracker welding machine/device for LPG (Liquefied Petroleum Gas) cylinders. The machine or device is designed to avoid various hazardous health issues that are found in the workers due to unfavorable working conditions during the welding process because of high temperature, dangerous fumes, and a large amount of luminous flux in the workstation. This machine involves a pneumatic holder to hold the cylindrical workpiece inside a metallic body frame. When the cylindrical workpiece rotates in a pneumatic holder, some amount of deflection arises due to the improper arrangement of fixtures and cylinders. It is not easy to overcome this deflection in fixtures, and due to this, the welding process is not done correctly. The undercut and overcut errors can also be found due to the improper shape of fixtures and cylinders. Therefore, the box setup is designed to cope with this problem, and the box contains an RGB (Red Green Blue) camera, LED (Light Emitting Diode), and an ultrasonic sensor. The box is installed in front of the welding torch to capture the image of the seam path of the workpiece. Firstly, the image is created with the help of a LED fitted inside the box. The LED creates the shadow of the joggle joint of the cylindrical workpiece. The camera captures that shadow. After capturing the image of shadow, the centroid of the seam path (represented by shadow) of the cylindrical workpiece is traced. It is sent to the Raspberry Pi microcontroller, and the data of the image is stored in the memory of the microcontroller for further processing. According to the position of the centroid coordinate, the microcontroller sends the control commands to the microstep driver of the stepper motor to follow this centroid coordinate. By this, the torch is controlled, and the welding process is done autonomously. The experimental setup is made, and the autonomous welding process is performed in many cylinders. Moreover, after performing autonomous welding process in the cylinder, the high-pressure bursting test (which is basically done by cylinder manufacturer industries) is performed to verify the effectiveness and efficiency of the welding joints. In the bursting test result, it is found that the cylinder is burst in a longitudinal direction, which verifies the effectiveness and efficiency of the obtained circumferential joggle joints

Wheelchair Controlled by Speech and Vision

Abstract: The voice assistant module and eye tracking are both used in the smart wheelchair designed for individuals with disabilities. A module for image processing, a wheelchair-controlled voice associate module, and restricted by appliances are among the two modules found in the smart wheelchair. The wheelchair-mounted camera that can take pictures and handle those images makes up the picture processing module. To obtain pupil movement in the 2D bearing, the acquired image was put into a Raspberry Pi microcontroller process using Open CV. The student is then remotely moved to the wheelchair management module. Additionally, the procedures are controlled by the movement of the eyes. Technology such as speech recognition offers a means for people to communicate with wheelchairs.

METODE ONE TIME PASSWORD MODIFIED SEBAGAI SISTEM KEAMANAN MONITORING IOT

Monitoring is an activity carried out to find out the process of running a program or system that has been designed, whether it runs well as expected or not, find out the obstacles that occur and how to overcome these obstacles. The cryptographic scheme to secure communication protocols is one of the most effective defenses against a variety of attacks, including eavesdropping and simple routing attacks, at the communication layer. One of the techniques studied in cryptography is encryption-description. This study aims to apply a one time password (OTP) modified algorithm into an IoT based temperature and humidity monitoring system using a Raspberry PI microcontroller device and a DHT 11 sensor. . The encryption process begins with hashing using the SHA method on the plaintext, followed by producing a private key and public key for RSA Signature encryption, the hashing results will be encrypted using the RSA Signature algorithm, the RSA Signature encryption results will be combined with the RSA Signature public key and plaintext, followed by producing an OTP that will be used as an AES key. Next the AES encryption process is executed, the OTP key will be encrypted RSA, the results of the RSA ciphertext and the AES ciphertext are combined and encoded with the Base64 method. The implementation of this security system gets the test results of the average duration for data encryption process is 435,163ms, and the data transmission with encryption process is 123,705ms

Low-Cost Internet of Things Platform for Epilepsy Monitoring Using Real-Time Electroencephalogram

This work is focusing to develop a portable, low-cost remote diagnostic system for developing countries where the current state of health is not in the advanced stage. People with diseases like epilepsy, Alzheimer’s, an extreme turmeric state, or a disorder that makes it difficult to move have been observed. The authors propose a cost-effective remote neurology assessment health care system. To predict epilepsy form electroencephalogram (EEG) signals in real-time. The authors implemented the machine learning model that has been deployed in the raspberry pi micro-controller. The feature extraction stage was carried out in Matlab. The extracted features from the EEG signals were transferred wirelessly to the model deployed in pi raspberry to clearly predict epilepsy and normality cases. The results of the real-time prediction of the trained and deployed model were provided for the remote diagnosis system. The data visualizations can be done on Android/IOS and Matlab desktop.

Bio-Medical Waste Management System

Abstract: Sorting of products is a very difficult industrial process. Continuous manual sorting creates consistency issues. This paper describes a working prototype designed for automatic sorting of objects based on the colour. We are using Pi camera to detect the object and the Raspberry Pi microcontroller was used to control the overall process. The identification of the biomedical Waste is based on the image processing using Pi camera module. Two conveyor belts were used, each controlled by separate Servo motors. The first belt is for placing the product to be analysed by the pi camera module, and the second belt is for moving the container, having separated compartments, in order to separate the products. The experimental results promise that the prototype will fulfil the needs for higher production and precise quality in the field of automation. Keywords: Raspberry pi, Servo Motor, Pi camera, Programming

Multi-Functional Agribot

Abstract: This project aims to design an agricultural robot to perform operations such as plowing, seeding, leveling, pesticide spraying, and plant health monitoring. The proposed Agribot is built by using the Raspberry Pi microcontroller. The microcontroller, the heart of the robotic system can manipulate the whole actions of the system. A motor driving circuit is used to drive the DC motors which in turn control the wheel motion. The Raspberry pi is given with a high-tech web camera for capturing the image, since the resolution of the given Raspberry pi camera is not sufficient to get the clarity image, Wi-Fi is used for the manual control of the robot. The Agribot is powered using a 12V battery. This 12V is used for the functioning of all other components like Relay, Motors, and Motor Drivers. Keywords: Robotic system, Raspberry Pi, Wi-Fi, Web Camera, Plant health monitoring.

Analysis of Microenvironment Data Using Low-Cost Portable Data Logger Based on a Microcontroller

Understanding the ecological and species diversity of natural places also requires the data of natural environmental factors, but due to environmental and geographical conditions, monitoring the micro-environment can be difficult. In this context, we propose a portable and cost-effective weather station based on the micro-controller Raspberry Pi Pico mounted with a digital temperature and humidity DHT11 sensor and a portable battery for monitoring and studying the effect of micro-climates on the forest, plant nursery, and farmland. The developed prototype is tested by collecting the temperature and humidity data in the control and random environment. A seasonal decomposition using moving averages on time series is done determining the trend, seasonal, and noise components. We perform the multi-fractal detrended fluctuation analysis on the collected data and estimated the Hurst exponent to check for the trends and patterns and their variation for different environments.

Development of Computer Vision Algorithms for Multi-class Waste Segregation and Their Analysis

Classification of waste for recycling has been a focal point for scientists interested in the field of conservation of the environment. Recycling consists of numerous steps, of which one of the most crucial is the segregation of recyclables from all other waste. Due to a lack of safety standards in developing countries, waste collection is often done manually by domestic helpers, or "rag-pickers". Such a process risks individual and public health. The waste collection methods may ultimately cause waste to become non-recyclable due to cross-contamination. Literature shows that research in this direction focuses on a single class of waste detection. The proposed work investigates CNN, YOLO, and faster RCNN-based multi-class classification methods to detect different types of waste at the collecting point. The smart dustbin proposed employs these computer vision methods with a Raspberry Pi microcontroller and camera module. The experimental results for multi-class classification show that the CNN has 80% of accuracy with 60% of the loss. Whereas the YOLO algorithm shows an accuracy of 88% and a loss of 40%. But the best results were obtained from faster RCNN object detection with API, with an accuracy of 91% and a loss of 16%. There is already an existing method for making a smart dustbin, so the results are compared to show how computer vision can be used to make a smart dustbin. This shows how computer vision can be used to make a smart dustbin. Doi: 10.28991/ESJ-2022-06-03-015 Full Text: PDF

Internet of Things in E-Healthcare System

Health awareness is important with increasingly complex challenges towards maintaining good health, especially during the COVID-19 pandemic. Regular health monitoring is a must for everyone during the pandemic. This project proposed an IoT based E-Healthcare system to monitor the body temperature, blood pressure and heartbeat rate. The temperature sensor was connected to a Raspberry Pi microcontroller and the readings were stored in the SQLite database management system. The blood pressure and heartbeat rate readings were retrieved from the wrist blood pressure sensor and the readings were entered into the database. The user can access the health data record through a smartphone.

A Review on AI Vision Robotic Arm Using Raspberry Pi

Abstract: This article offers a project to format a strong robot arm that would perform multifunctional tasks. The purpose of the project is to interest all axes of manipulator to lift, supply and promote off the objects at a desired location. This requires a unique strain motion control that incorporate electric powered vehicles as a strain machine. Further experiments are achieved to implement a digital digicam based completely 3D vision machine blanketed with a computer vision set of policies to recognize object deformation and spatial coordination to control the deviation from the precise training. The vision machine requires a separate computing hardware capable of processing complex vision algorithms. We make use of Raspberry pi microcontroller for processing the vision data, one at a duration making the vision machine capable of recognizing the precise object as constant with software program commands. Keywords: 6 DOF; robotic arm; computer vision system; color detection; object recognition

Deep Neural Network Based Linearization and Cold Junction Compensation of Thermocouple

In this proposed work, temperature sensors, namely, a thermocouple and thermistor were linearized using deep neural networks. The deep feedforward neural network (DFNN) technique was proposed to linearize the K-type thermocouple’s output, in the given temperature range −100 °C to 1372 °C, while nonlinearity was reduced from 2.03% to 0.002% full scale span (FSS). Deep layer recurrent neural network (DLR-NN) was used to reduce the nonlinearity of a negative temperature coefficient (NTC) thermistor from 84.63% to 0.13% FSS. The linearized thermistor was used for cold junction compensation (CJC) of the thermocouple. In both the thermocouple and thermistor, linearization was achieved in a single stage for a wide range digitally using deep neural networks alone. There were no analog pre-signal conditioning circuits, unlike the existing neural network-based linearization techniques in literature. A hardware setup of a stand-alone module for linearization was designed using the Raspberry pi microcontroller consisting of two soft modules, one for thermocouple linearization and the other for thermistor linearization. The proposed system was experimentally tested using a K-type thermocouple on a thermal calibrator in a 0 °C–300 °C range. The cold junction compensated output of the thermocouple had a maximum absolute error of 0.34 °C when ambient temperature varied from 0 °C to 40 °C. The results were satisfactory and better than the existing National Institute of Standards and Technology (NIST) standard. This linearization technique can be extended to other thermocouple types as well as other nonlinear sensors.

Intelligent Offline Multi Object Recognition Walking Stick for The Blind

Vision is one of the most important characteristics of a human that aid their day to day activities. Loss of vision however affects the ability of humans to freely navigate their environment and recognized objects along their path. Existing object recognition systems for the blind are mostly cloud based and its performance depends on reliable internet access. This makes them unsuitable in places with unreliable internet. Therefore, in this paper, a multi-object recognition intelligent walking stick for the blind that is completely independent of the internet was developed. The system is divided into three units, detection, recognition and communication units. The detection unit make use of an ultrasonic sensor and a buzzer, for informing the user of an impending obstacle. The recognition system makes use of a camera for capturing images with Convolutionary Neural Network architecture and Mobile Network Single Shot Multi-Box Detector (MobileNet SSD) for detecting objects in images. The communication unit transmits the recognised objects through voice to the user in English Language. The entire system was deployed in a Raspberry Pi microcontroller due to its processing power. The result obtained from testing of the device on the field showed that the recognition unit achieved an average sensitivity, specificity, precision and accuracy of 87.26%, 67.45%, 89.07%, 82.50% respectively. This shows that the system is reliable and can be used in recognizing objects for the blind.

PERANCANGAN ALAT PENDETEKSI PENEBANG LIAR MENGGUNAKAN SENSOR SUARA BERBASIS IoT-RASPBERRY PI

Protected forest areas have the main function of protecting life support systems. The protected forest area of ??Ekasari Jembrana Bali has an area of ??1.603 hectares. In the Ekasari protected forest there are many illegal logging actions. In 2020 there were two cases of wood theft. The types of wood stolen were reed and rosewood. In addition to these two cases, in 2018 to 2019 there were more than 10 cases of illegal logging that occurred, it is necessary to have a tool that is able to detect the occurrence of illegal logging in protected forests. The working system of this tool uses a Pi NoIR camera module to take pictures and a sound sensor FC-04 to detect the sound of a chainsaw with a value of 55 dB. This tool uses a Raspberry Pi microcontroller which functions to receive and process data, so that it is able to display warnings on websites and telegram applications. This tool has a detection range of 1,1304 hectares, so 1.418 tools are needed in the entire protected forest area of ??Ekasari Jembrana Bali.

Smart Healthcare Security Device on Medical IoT Using Raspberry Pi

This paper aims to improve the protection of two-wheelers. This study is divided into two parts: a helmet unit and a vehicle unit. The primary unit is the helmet unit, which contains a sensor, and the second part is known as the alcohol sensor, which is used to determine whether or not the driver is wearing the user helmet correctly. This data is then transmitted to the vehicle unit via the RF transmitter. The data is encoded with the aid of an encoder. Suppose the alcohol sensor senses that the driver is intoxicated. In that case, the IoT-based Raspberry Pi micro-controller passes the data to the vehicle unit via the RF transmitter, which immediately stops the vehicle from using the Driver circuit to control the relay. To stop the consumption of alcohol, the vehicles would be tracked daily. If the individual driving the vehicle is under the influence of alcohol while driving, the buzzer will automatically trigger. The vehicle key will be switched off.

Sistem Kendali Pengatur Suhu Ruangan pada Smart Building dengan Aplikasi Telegram menggunakan Fuzzy Logic Control

Rapid technological advances have resulted in many changes that occur in everyday life, this arises from the existence of creative ideas combined with things that are around and existing technological developments. For example, in regulating room temperature, namely adjusting the temperature in an air conditioner so that it can be used efficiently. This room temperature controller is basically done manually by adjusting the temperature according to the needs of the room. Basically, for temperature regulation, the principle of adding or subtracting degrees to the temperature control device is used. This is done manually by humans, so if there are many rooms that are required to use the room temperature control device, it takes a lot of time and calm to be issued. From this, an idea emerged to create a room temperature control system that could be controlled using the Telegram application. In this case, the fuzzy logic method is used to perform data presentation and processing in combination with the Raspberry PI microcontroller. According to the test results, humidity and temperature in the room can be captured correctly, and the system can regulate humidity and temperature in each room in the building through the Telegram application, so that it can overcome the problem of inefficient room temperature regulation.

Multifunctional And Low Cost Braille Display

Braille Display is a device which helps the visually impaired to read a text file in Braille format and as well receive an audio output of the text file. It gives an option to the user whether to receive output in terms of braille or audio. Braille is a tactile writing language of raised dots using which blinds and visually impaired people read and write through touch. The pattern of raised dots can be read with the fingers by blinds. The number and arrangement of these dots forms alphabets, numbers and special characters. The braille output in Braille display is observed in Braille text popper device which is made up of a plate having holes. Solenoids are placed under each hole to raise or lower the braille pin which will emerge through perforations on the top plate to form a Braille dot. The perforations serve as guides for the pins and form six dots which is equivalent to one Braille cell. Raspberry Pi Microcontroller is used to convert the text file into Braille format and thus controls the corresponding solenoid present below each of the holes to either pop up or down the hole by sending signal values to solenoids. Thus the solenoids align themselves based on the input character from the text file . The blinds can feel the sense of touch of the Braille pins that are popped up and they recognize the character accordingly. This way, blinds can recognize character by character. At every iteration, three characters from text file comes out in braille in Braille text popper device. The next button would display next three characters and previous button would display previous three characters. If the user prefers to hear audio, then it would give audio output of the text file through ear phones. Thus, the device facilitates the user either to read the text in braille or to hear the text as audio making it multifunctional.