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IoT Based Monitoring and Controlling Mechanism for Orange Orchard
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Environmental sanitation is very essential for healthy living. In our daily livelihood, garbage bins are usually kept without proper monitoring until they are filled to the point of overflowing onto the surroundings and spilling out, resulting in environmental pollution, which has serious health-related issues to human beings and the environment. For smart cities, garbage bins need to be monitored and controlled to ensure a healthy and clean environment. In the present technological advancement, real-time monitoring and control of waste disposal is a challenging area that needs urgent attention by the research community. The traditional approach of monitoring waste in garbage bins placed in strategic locations is a very tedious and inefficient way that consumes time, human effort, and cost, and this is also not in agreement with smart city requirements. This research paper presents the design and implementation of an internet of things (IoT) based Arduino microcontroller working with the ultrasonic sensors that detects the level of waste in the garbage bin placed in garbage locations and constantly at regular intervals display the status information as "filled", "half-filled", or "empty" on an LCD screen, as well as send the content level information at those intervals to a central web-server system that displays the garbage bin levels graphically. This is achieved using a microcontroller, a Wi-Fi module, and ultrasonic sensors. The programming of the Arduino Uno microcontroller was done with an Arduino IDE and embedded C programming language. The communication with the web server was done using the hypertext preprocessor PHP scripting programming language. The prototype was designed and simulated using Proteus 8.0 professional simulation software. This process helps to automate garbage bin monitoring and control. Experimental results demonstrate a promising solution to waste management and control. A number of testing runs were performed to evaluate the device workability in real situations. The measured distances from the garbage bins were transmitted to a website; this web page performs analytic and visualization and displays a bar chart showing the levels of the garbage waste, time, and location in real time for viewing. The proposed prototype is an innovative system that will help to keep the smart cities clean and tidy using ultrasonic sensors.
This research paper presents the design and implementation of an internet of things-based (IoT) smart framework for human heartbeat rate monitoring and control system. A comprehensive study of various techniques and technologies that are used in controlling the heartbeat rate is explored. The proposed system was designed and implemented on a breadboard with the various system components that are assembled, connected and tasted. Experimental results obtained from the implemented prototype were found to be accurate, as the system was able to sense and read the heartbeat rate of its user and transmit the sensed data through the internet. The system components were soldered on a breadboard, and cased inside a plastic container with the heart pulse sensor stretched, so as to be clipped on the fingertip of the system’s user. Experimental results demonstrate that the resting heartbeat rate of children below the age of 17 is between 65 to 115 beats per minute (bpm) and the resting heartbeat rate of an adult between the ages of 17 to 60 is 60 to 100 bpm. In addition, the resting heartbeat rate of old people who are 60 years old and above, their heartbeat rate is between 65 to 120 bpm. These findings are in agreement with the state-of-the-art in the medical field. Furthermore, this research paper presents an approach that is flexible, reliable, and confidential for heartbeat rate monitoring and control system using sensor network and IoT technology which can be deployed to the medical field to assist the medical practitioners in doing their work easily.
Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.
Nowadays, our environment is suffering from environmental pollution, especially air and sound
pollution. This is on a rapid increase due to speedy growth in industrial and human activities across the universe.
Therefore, a monitoring and control system is recommended to bring these pollutants under control. This paper
presents an IoT-Based framework for environmental pollution monitoring and control system that can detect and
monitor both the level of sound and the existence of harmful gases in the environment. The paper extended our
previous conference paper work [15]. The methodology employs the use of MQ-2 gas and LMVR sound sensors
and a real working prototype was designed and implemented. The Proteuos 8.0 professional and embedded C
programming language are used as the development tools. The hardware architecture consists of the Arduino UNO
board with hardware support packages, Wi-Fi module and a web server for monitoring pollution levels. The
implemented prototype was tested for two parameters, namely, noise and smoke levels. This is achieved by
comparing the threshold value with the normal behavior levels of the sensors. This process provided a real time
data for monitoring and control of pollution, which make the environment smart and conducive for living. Realtime
measurements of the two parameters are taken as long as the system prototype is on and connected to the
internet. A Wi-Fi module was used to provide internet access and data synchronization between sensors. The
sensor data can be monitored and control from remote locations via the internet. Experimental results and case
study, demonstrates the flexibility and efficiency of the proposed system. These findings will help to plan for a
healthy surrounding so that smart-environment users will be able to control the pollution by taking necessary
measures to reduce the level of pollutants for smart environments. With the implemented device prototype, it’s
very easy to monitor and control the level of air and sound pollution present in the environment.
In recent years, intelligent sensor techniques have achieved significant attention in agriculture. It is applied in agriculture to plan the several activities and missions properly by utilising limited resources with minor human interference. Currently, plant cultivation using new agriculture methods is very popular among the growers. However, the aeroponics is one of the methods of modern agriculture, which is commonly practiced around the world. In the system, plant cultivates under complete control conditions in the growth chamber by providing a small mist of the nutrient solution in replacement of the soil. The nutrient mist is ejected through atomization nozzles on a periodical basis. During the plant cultivation, several steps including temperature, humidity, light intensity, water nutrient solution level, pH and EC value, CO 2 concentration, atomization time, and atomization interval time require proper attention for flourishing plant growth. Therefore, the object of this review study was to provide significant knowledge about early fault detection and diagnosis in aeroponics using intelligent techniques (wireless sensors). So, the farmer could monitor several paraments without using laboratory instruments, and the farmer could control the entire system remotely. Moreover, the technique also provides a wide range of information which could be essential for plant researchers and provides a greater understanding of how the key parameters of aeroponics correlate with plant growth in the system. It offers full control of the system, not by constant manual attention from the operator but to a large extent by wireless sensors. Furthermore, the adoption of the intelligent techniques in the aeroponic system could reduce the concept of the usefulness of the system due to complicated manually monitoring and controlling process.
A highly sensitive Gallium Nitride (GaN) diaphragm based micro-scale pressure sensor with an AlGaN/GaN heterostructure field effect transistor (HFET) deflection transducer has been designed and fabricated for high temperature applications. The performance of the pressure sensor was studied over a pressure range of 20 kPa, which resulted in an ultra-high sensitivity of ~0.76%/kPa, with a signal-to-noise ratio as high as 16 dB, when biased optimally in the subthreshold region. A high gauge factor of 260 was determined from strain distribution in the sensor membrane obtained from finite element simulations. A repeatable sensor performance was observed over multiple pressure cycles up to a temperature of 200 °C.
For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.
Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.
We present here the design and fabrication of a self-powered and autonomous fringing field capacitive sensor to measure soil water content. The sensor is manufactured using a conventional printed circuit board and includes a porous ceramic. To read the sensor, we use a circuit that includes a 10 kHz triangle wave generator, an AC amplifier, a precision rectifier and a microcontroller. In terms of performance, the sensor’s capacitance (measured in a laboratory prototype) increases up to 5% when the volumetric water content of the porous ceramic changed from 3% to 36%, resulting in a sensitivity of S = 15.5 pF per unity change. Repeatability tests for capacitance measurement showed that the θ v sensor’s root mean square error is 0.13%. The average current consumption of the system (sensor and signal conditioning circuit) is less than 1.5 μ A, which demonstrates its suitability for being powered by energy harvesting systems. We developed a complete irrigation control system that integrates the sensor, an energy harvesting module composed of a microgenerator installed on the top of a micro sprinkler spinner, and a DC/DC converter circuit that charges a 1 F supercapacitor. The energy harvesting module operates only when the micro sprinkler spinner is irrigating the soil, and the supercapacitor is fully charged to 5 V in about 3 h during the first irrigation. After the first irrigation, with the supercap fully charged, the system can operate powered only by the supercapacitor for approximately 23 days, without any energy being harvested.
Chongqing mountain citrus orchard is one of the main origins of Chinese citrus. Its planting terrain is complex and soil parent material is diverse. Currently, the citrus fertilization, irrigation and other management processes still have great blindness. They usually use the same pattern and the same formula rather than considering the orchard terrain features, soil differences, species characteristics and the state of tree growth. With the help of the ZigBee technology, artificial intelligence and decision support technology, this paper has developed the research on the application technology of agricultural Internet of Things for real-time monitoring of citrus soil moisture and nutrients as well as the research on the integration of fertilization and irrigation decision support system. Some achievements were obtained including single-point multi-layer citrus soil temperature and humidity detection wireless sensor nodes and citrus precision fertilization and irrigation management decision support system. They were applied in citrus base in the Three Gorges Reservoir Area. The results showed that the system could help the grower to scientifically fertilize or irrigate, improve the precision operation level of citrus production, reduce the labor cost and reduce the pollution caused by chemical fertilizer.
Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400–2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R 2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m −1 and an RPD of 2.21, and was more suitable for detecting salinization (10–20 dS·m −1) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.
Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.
Agriculture sector is evolving with the advent of the information and communication technology. Efforts are being made to enhance the productivity and reduce losses by using the state of the art technology and equipment. As most of the farmers are unaware of the technology and latest practices, many expert systems have been developed in the world to facilitate the farmers. However, these expert systems rely on the stored knowledge base. We propose an expert system based on the Internet of Things (IoT) that will use the input data collected in real time. It will help to take proactive and preventive actions to minimize the losses due to diseases and insects/pests.
We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.
Substrate volumetric water content (VWC) is a useful measurement for automated irrigation systems. We have previously developed automated irrigation controllers that use capacitance sensors and dataloggers to supply plants with ondemand irrigation. However, the dataloggers and accompanying software used to build and program those controllers make these systems expensive. Relatively new, low-cost open-source microcontrollers provide an alternative way to build sensorbased irrigation controllers for both agricultural and domestic applications. We designed and built an automated irrigation system using a microcontroller, capacitance soil moisture sensors, and solenoid valves. This system effectively monitored and controlled VWC over a range of irrigation thresholds (0.2, 0.3, 0.4, and 0.5 m3.mL3) with ‘Panama Red’ hibiscus (Hibiscus acetosella) in a peat:perlite substrate. The microcontroller can be used with both regular 24-V alternating current (AC) solenoid valves and with latching 6- to 18-V direct current (DC) solenoid valves. The technology is relatively inexpensive (microcontroller and accessories cost 440, and four solenoid valves cost 667) and accessible. The irrigation controller required little maintenance over the course of a 41-day trial. The low cost of this irrigation controller makes it useful in many horticultural settings, including both research and production
A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.
In this paper, we propose developing a system optimally watering agricultural crops based on a wireless sensor network. This work aimed to design and develop a control system using node sensors in the crop field with data management via smartphone and a web application. The three components are hardware, web application, and mobile application. The first component was designed and implemented in control box hardware connected to collect data on the crops. Soil moisture sensors are used to monitor the field, connecting to the control box. The second component is a web-based application that was designed and implemented to manipulate the details of crop data and field information. This component applied data mining to analyze the data for predicting suitable temperature, humidity, and soil moisture for optimal future management of crops growth. The final component is mainly used to control crop watering through a mobile application in a smartphone. This allows either automatic or manual control by the user. The automatic control uses data from soil moisture sensors for watering. However, the user can opt for manual control of watering the crops in the functional control mode. The system can send notifications through LINE API for the LINE application. The system was implemented and tested in Makhamtia District, Suratthani Province, Thailand. The results showed the implementation to be useful in agriculture. The moisture content of the soil was maintained appropriately for vegetable growth, reducing costs and increasing agricultural productivity. Moreover, this work represents driving agriculture through digital innovation.
past few years, automatic irrigation system has seen a rapid growth in terms of technology. At present cost-saving technology, labor-saving are the addressing key issues in irrigation. This paper gives a review of these systems based on existing technologies and also proposes an economical and generic automatic irrigation system based on wireless sensors with GSM-Bluetooth for irrigation system controller and remote monitoring system. This system has simpler features designed with the objective of low cost and effective with less power consumption using sensors for remote monitoring and controlling devices which are controlled via SMS using a GSM module. A Bluetooth module is also interfaced with the main microcontroller chip. This Bluetooth module eliminates the usage charges by communicating with the appliances via Bluetooth when the application is in a limited range of few meters. The system informs user about any abnormal conditions like less moisture content and temperature rise, even concentration of CO2 via SMS from the GSM module or by Bluetooth module to the farmer's mobile and actions are taken accordingly by the farmer. In future, the farmer will be able to monitor and control the parameter by GSM and Bluetooth technologies. KeywordBluetooth, remote monitoring, Sensors, Microcontroller, Agriculture
A Review on Automated Irrigation System Using Wireless Sensor Network
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IoT Based Automated IrrigationSystem
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A Low-Cost Smart IrrigationControl System
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Review Paper Based on Automatic Irrigation System Based on RF Module
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Automated Irrigation System in Agriculture Using Wireless Sensor Technology
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Arduino Based Automated Watering System
- Sumeet
Design of an Automated Irrigation System: Student Paper Competition
- Marie
A Review on Automated Irrigation System Using Wireless Sensor Network
- G G Yogesh
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Arduino Based Automated Watering System
- S Sumeet
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Design of an Automated Irrigation System: Student Paper Competition
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- Canada
Review Paper Based on Automatic Irrigation System Based on RF Module
- R Deweshvree
- P R Indurkar
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Soil Moisture and Nutrients Using an IoT Based System
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