scholarly journals Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System

2020 ◽  
Vol 12 (4) ◽  
pp. 1433 ◽  
Author(s):  
Xue-Bo Jin ◽  
Xing-Hong Yu ◽  
Xiao-Yi Wang ◽  
Yu-Ting Bai ◽  
Ting-Li Su ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Precision Agriculture ◽  
Agricultural Production ◽  
Future Trend ◽  
Weather Data ◽  
Multiple Components ◽  
Proposed Model ◽  
Decomposition Structure ◽  
And Control

Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production.

IoT-based Precision Agriculture Platform: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 1419-1421
Author(s):  
Rutvik Solanki
Keyword(s):  
Internet Of Things ◽  
Precision Agriculture ◽  
Agricultural Sector ◽  
Disease Outbreaks ◽  
Iot Platforms ◽  
High Level ◽  
New Applications ◽  
And Control ◽  
Use Of Internet ◽  
The Internet Of Things

Abstract: Technological advancements such as the Internet of Things (IoT) and Artificial Intelligence (AI) are helping to boost the global agricultural sector as it is expected to grow by around seventy percent in the next two decades. There are sensor-based systems in place to keep track of the plants and the surrounding environment. This technology allows farmers to watch and control farm operations from afar, but it has a few limitations. For farmers, these technologies are prohibitively expensive and demand a high level of technological competence. Besides, Climate change has a significant impact on crops because increased temperatures and changes in precipitation patterns increase the likelihood of disease outbreaks, resulting in crop losses and potentially irreversible plant destruction. Because of recent advancements in IoT and Cloud Computing, new applications built on highly innovative and scalable service platforms are now being developed. The use of Internet of Things (IoT) solutions has enormous promise for improving the quality and safety of agricultural products. Precision farming's telemonitoring system relies heavily on Internet of Things (IoT) platforms; therefore, this article quickly reviews the most common IoT platforms used in precision agriculture, highlighting both their key benefits and drawbacks

scholarly journals A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

2021 ◽  
pp. 182-189
Author(s):  
S. Arokiaraj ◽  
Dr. N. Viswanathan
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Short Term Memory ◽  
The Other ◽  
Learning Models ◽  
Computational Overhead ◽  
Temporal Features ◽  
Human Movements ◽  
Proposed Model ◽  
Iot Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

The Design of Modern Agriculture Control System Based on Internet of Things

2014 ◽  
Vol 513-517 ◽  
pp. 1519-1522 ◽  
Author(s):  
Bin Peng Wang
Keyword(s):  
Control System ◽  
Internet Of Things ◽  
Precision Agriculture ◽  
Agricultural Production ◽  
Management System ◽  
High Efficiency ◽  
Modern Society ◽  
Wireless Data ◽  
Modern Agriculture ◽  
Wireless Data Transmission

This paper involves the modern agricultural application control system which is based on internet of things, and this intelligent management system uses intelligent control technology such as S7-300, GSM,WSN and Zigbee to realize the modernization of rural security, agricultural production and residents living fully intelligent managed. This system applies precision agriculture, digital image processing, wireless data transmission and other fields, really combining digital management technology with embedded technology. At the same time, this system which is based on internet of things is the necessary path of modern agriculture informatization strategy. With the mature development of technology of internet of things in modern society, modern agriculture application management system based on internet of things will bring new change to agriculture and high efficiency of agricultural production.

scholarly journals Precision Agriculture and Irrigation: Current U.S. Perspectives

2020 ◽  
Vol 63 (1) ◽  
pp. 57-67
Author(s):  
Steven R. Evett ◽  
Susan A. O’Shaughnessy ◽  
Manuel A. Andrade ◽  
William P. Kustas ◽  
M. C. Anderson ◽  
...  
Keyword(s):  
Decision Support ◽  
Internet Of Things ◽  
Precision Agriculture ◽  
Water Productivity ◽  
Nutrient Use Efficiency ◽  
List Type ◽  
Nutrient Use ◽  
Use Efficiency ◽  
And Control ◽  
The Internet Of Things

Highlights.Precision agriculture (PA) applications in irrigation are stymied by lack of decision support systems.Modern PA relies on sensor systems and near real-time feedback for irrigation decision support and control.Sophisticated understanding of biophysics and biological systems now guides site-specific irrigation.The internet of things (IOT) enables new ways to increase yield per unit of water used and nutrient use efficiency. Keywords: Crop water productivity, Decision support system, Internet of things, Remote sensing, SCADA, Soil water content.

scholarly journals RF Energy Harvesting IoT System for Museum Ambience Control with Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4465 ◽  
Author(s):  
Nermeen A. Eltresy ◽  
Osama M. Dardeer ◽  
Awab Al-Habal ◽  
Esraa Elhariri ◽  
Ali H. Hassan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Energy Harvesting ◽  
Internet Of Things ◽  
Radio Frequency ◽  
Electric Power ◽  
Sensor Nodes ◽  
Monitoring And Control ◽  
Rf Energy Harvesting ◽  
Rf Energy ◽  
And Control

Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the health of the artifacts and provide alarms upon intended or unintended vandalization attempts, but also allows for remote ambience control through authorized Internet-enabled devices. A key differentiating aspect of the proposed system is the use of always-on and power-hungry sensors for comprehensive and precise museum monitoring, while being powered by harvesting the Radio Frequency (RF) energy freely available within the museum. This contrasts with technologies proposed in the literature, which use RF energy harvesting to power simple IoT sensing devices. We use rectenna arrays that collect RF energy and convert it to electric power to prolong the lifetime of the sensor nodes. Another important feature of the proposed system is the use of deep learning to find daily trends in the collected environment data. Accordingly, the museum ambience is further optimized, and the system becomes more resilient to faults in the sensed data.

scholarly journals DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

2021 ◽  
Author(s):  
Santhadevi D ◽  
B
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Internet Of Things ◽  
Cyber Security ◽  
Malware Detection ◽  
Manufacturing Industries ◽  
Apache Spark ◽  
Learning Framework ◽  
Proposed Model ◽  
Confidential Data

Abstract Internet of Things (IoT) technology has a dynamic atmosphere due to incorporating multiple smart peripherals, which provide autonomous homes, cities, manufacturing industries, medical domain, etc.; however, a threat by cyber security is still at constant risk, and it gets much attention in researches. Cyber issues in the IoT environment are usually coming due to intruder’s malware activity. This kind of malware affects the confidential data of users in the IoT environment. In this research, a novel framework is implemented with the association of an improved deep LSTM with Harris Hawk Optimization (DLSTM-HHO). This framework is highly improved by adopting an Apache Spark technique for pre-processing IoT dataset. An Apache Spark replaces the traditional data pre-processing, which provides more efficiency to this model for detecting the malware at the edge of the IoT environment. The implementation of this framework is done in the MATLAB2020a platform with Apache Spark. The proposed model provides better performance evaluation in terms of accuracy is at 98%, and the F1-Score at 98.5%.

scholarly journals WSN and IoT Based Monitoring of Various Macronutrient Parameters and Disease Control of Banana Crop

2020 ◽  
Vol 9 (5) ◽  
pp. 1290-1296
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
Precision Agriculture ◽  
Banana Fruit ◽  
Complete Control ◽  
On Line ◽  
Manual Tasks ◽  
And Control ◽  
Banana Crop

In India, banana is an important fruit. In this research, we designed and develop a precision agriculture system to monitor the various macronutrients and various crucial parameters to control and early detection of various diseases of banana crop using Wireless Sensor Networks (WSN) and Internet of Things (IoT). Developed precision agriculture system is used various sensors to sense and measure various micronutrients like Magnesium (Mg), Calcium (Ca), Sulfur (S), nitrite content in soil, ground water quality, crop growth, pest detection, crop on line monitoring, animal intrusion into the field and so on. It also measures the different parameters like change in weather, temperature, humidity, moisture changes in soil, quality and fertility of soil, various weeds, and level of water. Precision agriculture system implemented using advance sensors and improved technologies like WSN, IoT. Research experimental results show significant improvement in quality of banana fruit and overall production of banana crop. Before design and implementation we have carried out a detailed literature review on various approaches of precision monitoring system using Internet of Things (IoT). Proposed precision agriculture system can be used to automate and complete control of all farming processes. Our major focused is on monitoring macronutrients like Magnesium (Mg), Calcium (Ca) and Sulfur (S) parameters, to supply balance macronutrients using automatic action and early detection of diseases and control of Banana Crops System which will result to increase the productivity and quality of Banana products. This precision agriculture system keep farmers/users updated and empowers with minimum manual tasks.

scholarly journals A Strategic Analytics Using Convolutional Neural Networks for Weed Identification in Sugar Beet Fields

2021 ◽  
Vol 13 (1) ◽  
pp. 49-57
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Asmaa Sarih ◽  
Adil Tannouche
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sugar Beet ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Large Scale ◽  
Aerial Images ◽  
Data Set ◽  
High Level ◽  
And Control

Researchers in precision agriculture regularly use deep learning that will help growers and farmers control and monitor crops during the growing season; these tools help to extract meaningful information from large-scale aerial images received from the field using several techniques in order to create a strategic analytics for making a decision. The information result of the operation could be exploited for many reasons, such as sub-plot specific weed control. Our focus in this paper is on weed identification and control in sugar beet fields, particularly the creation and optimization of a Convolutional Neural Networks model and train it according to our data set to predict and identify the most popular weed strains in the region of Beni Mellal, Morocco. All that could help select herbicides that work on the identified weeds, we explore the way of transfer learning approach to design the networks, and the famous library Tensorflow for deep learning models, and Keras which is a high-level API built on Tensorflow.

scholarly journals A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1039
Author(s):  
Prabesh Paudel ◽  
Sangkyoon Kim ◽  
Soonyoung Park ◽  
Kyoung-Ho Choi
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Action Recognition ◽  
Clean Energy ◽  
Context Aware ◽  
University Campus ◽  
Energy Usage ◽  
Demand And Supply ◽  
Power Load ◽  
And Control

With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings. This gives an extra edge on digitizing energy usage and, ultimately, reducing the power load in the electric grid. Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition. Using this method, we can save a significant amount of energy usage in buildings.

scholarly journals Energy Efficiency Analysis of Wireless Sensor Networks in Precision Agriculture Economy

2021 ◽  
Vol 2021 ◽  
pp. 1-7 ◽  
Author(s):  
Bingtao Zhang ◽  
Lingyan Meng
Keyword(s):  
Energy Efficiency ◽  
Sensor Networks ◽  
Precision Agriculture ◽  
Agricultural Production ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Long Distance ◽  
Information Platform ◽  
Wireless Protocols ◽  
Proposed Model

Wireless sensor network (WSN) can play an important role during precision agriculture production to promote the growth of the agricultural economy. The application of WSN in agricultural production can achieve precision agriculture. WSN has the biggest challenge of energy efficiency. This paper proposes a model to efficiently utilize the energy of sensor nodes in precision agriculture production. The proposed model provides a comprehensive analysis of the precision agriculture. The model focuses on the characteristics of WSN and expands its application in precision agriculture. In addition, this paper also puts forward some technical prospects to provide a good reference for comprehensively and effectively improving the overall development level of precision agriculture. The paper analyzes the applicability and limitations of the existing sensor networks used for agricultural production technology. The ZigBee and Lora wireless protocols are utilized to have the best power consumption and communication in short distance and long distance. Our proposed model also suggests improvement measures for the shortcomings of existing WSN in the context of energy efficiency to provide an information platform for WSN to play a better role in agricultural production.

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