scholarly journals Monitoring Potato Waste in Food Manufacturing Using Image Processing and Internet of Things Approach

2019 ◽  
Vol 11 (11) ◽  
pp. 3173 ◽  
Author(s):  
Sandeep Jagtap ◽  
Chintan Bhatt ◽  
Jaydeep Thik ◽  
Shahin Rahimifard
Keyword(s):  
Image Processing ◽  
Internet Of Things ◽  
Real Time ◽  
Load Cell ◽  
Test Accuracy ◽  
Waste Generation ◽  
Time Data ◽  
Potato Processing ◽  
Real Time System ◽  
Potato Waste

Approximately one-third of the food produced globally is spoiled or wasted in the food supply chain (FSC). Essentially, it is lost before it even reaches the end consumer. Conventional methods of food waste tracking relying on paper-based logs to collect and analyse the data are costly, laborious, and time-consuming. Hence, an automated and real-time system based on the Internet of Things (IoT) concepts is proposed to measure the overall amount of waste as well as the reasons for waste generation in real-time within the potato processing industry, by using modern image processing and load cell technologies. The images captured through a specially positioned camera are processed to identify the damaged, unusable potatoes, and a digital load cell is used to measure their weight. Subsequently, a deep learning architecture, specifically the Convolutional Neural Network (CNN), is utilised to determine a potential reason for the potato waste generation. An accuracy of 99.79% was achieved using a small set of samples during the training test. We were successful enough to achieve a training accuracy of 94.06%, a validation accuracy of 85%, and a test accuracy of 83.3% after parameter tuning. This still represents a significant improvement over manual monitoring and extraction of waste within a potato processing line. In addition, the real-time data generated by this system help actors in the production, transportation, and processing of potatoes to determine various causes of waste generation and aid in the implementation of corrective actions.

Sectional Data Acquisition and Control System for Solar Street Lamps Based on Internet of Things

2013 ◽  
Vol 773 ◽  
pp. 148-153 ◽  
Author(s):  
Juan Zhou ◽  
Bing Yan Chen ◽  
Meng Ni Zhang ◽  
Ying Ying Tang
Keyword(s):  
Control System ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Real Time ◽  
Network Flow ◽  
Short Interval ◽  
Management Problem ◽  
Time Data ◽  
Control Centre ◽  
And Control

Aiming at the management problem of real-time data created while intelligent solar street lamps working, sectional data acquisition and control system based on internet of things is introduced in the paper. Communication protocol with unified form and flexible function is designed in the system, and communication address is composed of sectional address and subsection address. Three-level data structure is built in the polling algorithm to trace real-time state of lamps and to detect malfunction in time, which is suitable for sectional lamps management characteristics. The system reflects necessary statistic data and exception information to remote control centre through GPRS to short interval expend on transmission and procession and save network flow and system energy. The result shows the system brings improved management affection and accords with the idea of energy-saving and environmental protection.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

scholarly journals Internet of Things for Smart Environment and Integrated Ecosystem

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1218
Author(s):  
Renu Thapliyal ◽  
Ravi Kumar Patel ◽  
Ajit Kumar Yadav ◽  
Akhilesh Singh
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Wireless Sensors ◽  
Home Automation ◽  
Time System ◽  
Real Time System ◽  
Introduction History ◽  
Virtual System ◽  
Increasing Demand ◽  
Real Time Application

Internet of things (IoT) is in increasing demand in our daily life. This is the technology that transforms the real-time system into the virtual system and makes the communication in between machines. The rapid growth of IoT can be easily noticed in industries like home automation, transport, robotics, environment, energy, water domain etc. The IoT is a technological revolution that represents the future of computing and communications, and its development depends on dynamic technical innovation in a number of important fields, from wireless sensors to nanotechnology. They are going to tag each object for identifying, automating, monitoring and controlling. The aim of this paper is to give an overview of introduction, history, architecture, real-time application, challenges and future aspects of IoT along with statistics and its application in monitoring for future.  

scholarly journals Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 649
Author(s):  
Yifeng Liu ◽  
Wei Zhang ◽  
Wenhao Du
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Quality Data ◽  
Time Data ◽  
High Quality ◽  
Real Time System ◽  
High Quality Data ◽  
Learning Framework ◽  
Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

scholarly journals Internet of Things (IoT) Platform for Multi-Topic Messaging

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3346
Author(s):  
Mahmoud Hussein ◽  
Ahmed I. Galal ◽  
Emad Abd-Elrahman ◽  
Mohamed Zorkany
Keyword(s):  
Operating System ◽  
Internet Of Things ◽  
Real Time ◽  
Communication Protocol ◽  
Real Time Systems ◽  
Real Time System ◽  
Client Server ◽  
Iot Applications ◽  
Iot Platforms ◽  
Time Systems

IoT-based applications operate in a client–server architecture, which requires a specific communication protocol. This protocol is used to establish the client–server communication model, allowing all clients of the system to perform specific tasks through internet communications. Many data communication protocols for the Internet of Things are used by IoT platforms, including message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), MQTT for sensor networks (MQTT-SN), data distribution service (DDS), constrained application protocol (CoAP), and simple object access protocol (SOAP). These protocols only support single-topic messaging. Thus, in this work, an IoT message protocol that supports multi-topic messaging is proposed. This protocol will add a simple “brain” for IoT platforms in order to realize an intelligent IoT architecture. Moreover, it will enhance the traffic throughput by reducing the overheads of messages and the delay of multi-topic messaging. Most current IoT applications depend on real-time systems. Therefore, an RTOS (real-time operating system) as a famous OS (operating system) is used for the embedded systems to provide the constraints of real-time features, as required by these real-time systems. Using RTOS for IoT applications adds important features to the system, including reliability. Many of the undertaken research works into IoT platforms have only focused on specific applications; they did not deal with the real-time constraints under a real-time system umbrella. In this work, the design of the multi-topic IoT protocol and platform is implemented for real-time systems and also for general-purpose applications; this platform depends on the proposed multi-topic communication protocol, which is implemented here to show its functionality and effectiveness over similar protocols.

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