scholarly journals DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture

2020 ◽  
Vol 10 (19) ◽  
pp. 6668
Author(s):  
Laura García ◽  
Lorena Parra ◽  
Jose M. Jimenez ◽  
Jaime Lloret ◽  
Pedro V. Mauri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Low Cost ◽  
Sensor Nodes ◽  
Flying Height ◽  
World Population ◽  
Node Density ◽  
Gateway Node ◽  
Crop Monitoring ◽  
Time Required

The increase in the world population has led to new needs for food. Precision Agriculture (PA) is one of the focuses of these policies to optimize the crops and facilitate crop management using technology. Drones have been gaining popularity in PA to perform remote sensing activities such as photo and video capture as well as other activities such as fertilization or scaring animals. These drones could be used as a mobile gateway as well, benefiting from its already designed flight plan. In this paper, we evaluate the adequacy of remote sensing drones to perform gateway functionalities, providing a guide for choosing the best drone parameters for successful WiFi data transmission between sensor nodes and the gateway in PA systems for crop monitoring and management. The novelty of this paper compared with existing mobile gateway proposals is that we are going to test the performance of the drone that is acting as a remote sensing tool to carry a low-cost gateway node to gather the data from the nodes deployed on the field. Taking this in mind, simulations of different scenarios were performed to determine if the data can be transmitted correctly or not considering different flying parameters such as speed (from 1 to 20 m/s) and flying height (from 4 to 104 m) and wireless sensor network parameters such as node density (1 node each 60 m2 to 1 node each 5000 m2) and antenna coverage (25 to 200 m). We have calculated the time that each node remains with connectivity and the time required to send the data to estimate if the connection will be bad, good, or optimal. Results point out that for the maximum node density, there is only one combination that offers good connectivity (lowest velocity, the flying height of 24 m, and antenna with 25 m of coverage). For the other node densities, several combinations of flying height and antenna coverage allows good and optimal connectivity.

scholarly journals Selecting IoT Connectivity Protocols for Rural Development Applications

2021 ◽  
Author(s):  
Vinod Kumar
Keyword(s):  
Energy Efficiency ◽  
Low Cost ◽  
Sensor Nodes ◽  
Node Density ◽  
Iot Applications ◽  
Smart Farming ◽  
Long Life ◽  
Networking Technology ◽  
Development Applications ◽  
Selection Of

Data sensing and collection over vast coverage areas form an integral part of IoT applications such as Smart Farming. Selection of adequate IoT connectivity technologies is an important step in the design process. Overall energy efficiency, availability of low-cost and long-life sensor nodes and achievability of long coverage range of the fixed infrastructure are the main criteria of selection. After a brief description of the scenario of connectivity technologies, this article demonstrates the usefulness of a Low Power Wide Area Networking technology named SigFox for the applications mentioned above. Performance figures in terms of coverage range and protocol throughput (manageable IoT node density) justify this claim.

scholarly journals Current research on Internet of Things (IoT) in Agriculture

2020 ◽  
Vol 9 (8) ◽  
pp. 882-886
Keyword(s):  
Power Supply ◽  
Precision Agriculture ◽  
Large Scale ◽  
Low Cost ◽  
Low Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Major Application ◽  
Sensing Time

IoT, a sensation in modern-day technology, has a major impact on the rapidly growing technological aspects. It’s making people’s life easier and also enabling us to do things that were previously seen as miracles. It helps in solving many complex real-time problems. One such major application in the field of agriculture can turn out to be productive and profitable. This paper explains a variety of techniques infusing IoT in agriculture, that helps in productive and predictive results in that field, thereby leading towards precision agriculture. A low-cost power supply and unambiguous farming can be possible with using IoT system. Wireless Sensor Networks (WSN) in which sensor nodes learn and adopt over the sensing time, gives simplicity, low energy usage. This is aimed to be deployed on a large scale by predicting using big data techniques from centralized data analysis.

Precision Agriculture

2021 ◽  
pp. 23-45 ◽  
Author(s):  
Waleed Fouad Abobatta
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Information Technology ◽  
Social Interaction ◽  
Precision Agriculture ◽  
Geographical Information Systems ◽  
Advanced Technology ◽  
Geographical Information ◽  
Farm Size ◽  
World Population

Precision agriculture is a management system that aims to reduce inputs like seeds, water, and energy; protect the environment; and maximize profitability. Precision agriculture uses advanced technology like positioning technology, geographical information systems, satellite navigation, and remote sensing. There are different factors affect the adoption of precision agriculture like farm size, legal affairs, and social interaction. Under climate change and increases in world population, adoption of precision agriculture could assist farmers to face various challenges to achieve ideal production and maximizing profitability. Information, technology, and management are considered the backbone of the precision agriculture system, and combining these elements reduces inputs and maximizes productivity. Different threats attacked precision agriculture including threats to confidentiality, threats to integrity, threats to availability, and crowding of the spectrum signal. This chapter explains the different roles of precision agriculture in developing agricultural production.

Research and Design of Forest Environment Monitoring System

2012 ◽  
Vol 518-523 ◽  
pp. 1592-1596
Author(s):  
Jun Wang ◽  
Xue Wen He ◽  
Peng Ju He
Keyword(s):  
Monitoring System ◽  
Low Cost ◽  
Implementation Process ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Forest Environment ◽  
Gateway Node ◽  
Protocol Conversion ◽  
Environmental Monitoring System ◽  
Wide Range

A forest environmental monitoring system based on GPRS communication network and ZigBee sensor network was researched and designed. The article described the architecture of the system, also discussed the framework of the sensor nodes using CC2430 as main chip and the gateway node based on MC35i.The data collection of sensor nodes, the protocol conversion of gateway node, and the implementation process of the monitoring center software were introduced in the article. With advantages of low cost, strong robustness and always online, the system can be widely used in a wide range for forest environmental parameters monitoring.

scholarly journals Node Density Based Security Level Determining to Prolong the Lifetime of WSN through Network Conditions

2020 ◽  
Vol 9 (9) ◽  
pp. 340-344
Keyword(s):  
Low Cost ◽  
Geographical Location ◽  
Security Protocols ◽  
Sensor Nodes ◽  
Security Level ◽  
Message Authentication ◽  
Authentication Code ◽  
Node Density ◽  
Geographical Environment ◽  
False Report

A wireless sensor network (WSN) consists of sensor nodes with low cost and limited performance. The energy needed for node management is important because it is difficult to manage the deployed nodes. Several traditional security protocols prevent several known attacks in WSNs, such as s false report injection attack. However, the traditional protocols do not consider the false traffic ratio and geographical environment factors. Therefore, to extend the network lifetime, a new design is needed that maintains security and considers environmental factors. This paper introduces a method to increase the energy efficiency and increase the detection rate of false reports by adjusting the security strength of the report generated at the node according to the node density and geographical location. The proposed method has the advantage that it can be applied to various security protocols based on Message Authentication Code (MAC).

scholarly journals CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 349-356 ◽  
Author(s):  
M. Hassanein ◽  
M. Khedr ◽  
N. El-Sheimy
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Weed Management ◽  
Low Cost ◽  
Color Space ◽  
Orientation Angle ◽  
Weed Detection ◽  
Agriculture Industry ◽  
Rgb Images ◽  
Scan Line

<p><strong>Abstract.</strong> Precision Agriculture (PA) management systems are considered among the top ten revolutions in the agriculture industry during the last couple decades. Generally, the PA is a management system that aims to integrate different technologies as navigation and imagery systems to control the use of the agriculture industry inputs aiming to enhance the quality and quantity of its output, while preserving the surrounding environment from any harm that might be caused due to the use of these inputs. On the other hand, during the last decade, Unmanned Aerial Vehicles (UAVs) showed great potential to enhance the use of remote sensing and imagery sensors for different PA applications such as weed management, crop health monitoring, and crop row detection. UAV imagery systems are capable to fill the gap between aerial and terrestrial imagery systems and enhance the use of imagery systems and remote sensing for PA applications. One of the important PA applications that uses UAV imagery systems, and which drew lots of interest is the crop row detection, especially that such application is important for other applications such as weed detection and crop yield predication. This paper introduces a new crop row detection methodology using low-cost UAV RGB imagery system. The methodology has three main steps. First, the RGB images are converted into HSV color space and the Hue image are extracted. Then, different sections are generated with different orientation angles in the Hue images. For each section, using the PCA of the Hue values in the section, an analysis can be performed to evaluate the variances of the Hue values in the section. The crop row orientation angle is detected as the same orientation angle of the section that provides the minimum variances of Hue values. Finally, a scan line is generated over the Hue image with the same orientation angle of the crop rows. The scan line computes the average of the Hue values for each line in the Hue image similar to the detected crop row orientation. The generated values provide a graph full of peaks and valleys which represent the crop and soil rows. The proposed methodology was evaluated using different RGB images acquired by low-cost UAV for a Canola field. The images were taken at different flight heights and different dates. The achieved results proved the ability of the proposed methodology to detect the crop rows at different cases.</p>

scholarly journals REAL-TIME AND POST-PROCESSED GEOREFERENCING FOR HYPERPSPECTRAL DRONE REMOTE SENSING

2018 ◽  
Vol XLII-2 ◽  
pp. 789-795 ◽  
Author(s):  
R. A. Oliveira ◽  
E. Khoramshahi ◽  
J. Suomalainen ◽  
T. Hakala ◽  
N. Viljanen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Precision Agriculture ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Target Object ◽  
Post Processing ◽  
The Real ◽  
Sensing Applications ◽  
Remote Sensing Applications

The use of drones and photogrammetric technologies are increasing rapidly in different applications. Currently, drone processing workflow is in most cases based on sequential image acquisition and post-processing, but there are great interests towards real-time solutions. Fast and reliable real-time drone data processing can benefit, for instance, environmental monitoring tasks in precision agriculture and in forest. Recent developments in miniaturized and low-cost inertial measurement systems and GNSS sensors, and Real-time kinematic (RTK) position data are offering new perspectives for the comprehensive remote sensing applications. The combination of these sensors and light-weight and low-cost multi- or hyperspectral frame sensors in drones provides the opportunity of creating near real-time or real-time remote sensing data of target object. We have developed a system with direct georeferencing onboard drone to be used combined with hyperspectral frame cameras in real-time remote sensing applications. The objective of this study is to evaluate the real-time georeferencing comparing with post-processing solutions. Experimental data sets were captured in agricultural and forested test sites using the system. The accuracy of onboard georeferencing data were better than 0.5&amp;thinsp;m. The results showed that the real-time remote sensing is promising and feasible in both test sites.

scholarly journals Crop Monitoring using Unmanned Aerial Vehicles: A Review

2021 ◽  
Author(s):  
Jose Cuaran ◽  
Jose Leon
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Environmental Changes ◽  
Great Opportunity ◽  
Crop Monitoring ◽  
Sensing Applications ◽  
Aerial Vehicles ◽  
Monitoring Applications ◽  
Common Application

Unmanned aerial vehicles (UAVs) or drones have been developed significantly over the past two decades, for a wide variety of applications such as surveillance, geographic studies, fire monitoring, security, military applications, search and rescue, agriculture, etc. In agriculture, for example, remote sensing by means of unmanned aerial vehicles has proven to be the most efficient way to monitor crops from images. Unlike remote sensing from satellite images or taken from manned aircraft, UAVs allow capturing images of high spatial and temporal resolution, thanks to their maneuverability and capability of flying at low altitude. This article presents an extensive review of the literature on crop monitoring by UAV, identifying specific applications, types of vehicles, sensors, image processing techniques, among others. A total of 50 articles related to crop monitoring applications of UAV in agriculture were reviewed. Only journal articles indexed in the Scopus database with more than 50 citations were considered. It was found that cereals are the most common crops where remote sensing has been applied so far. In addition, the most common crop remote sensing applications are related to precision agriculture, which includes the management of weeds, pests, diseases, nutrients and others. Crop phenotyping is also a common application of remote sensing, which consists of the evaluation of a crop’s physical characteristics under environmental changes, to select the plants or seeds with favorable genotype and phenotype. Besides, multirotor is the most common type of UAV used for remote sensing and RGB and multispectral cameras are mostly used as sensors for this application. Finally, there is a great opportunity for research in remote sensing related to a wide variety of crops, crop monitoring applications, vegetation indexes and photogrammetry.

scholarly journals Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying With Case Study of Palms

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhenwang Qin ◽  
Wensheng Wang ◽  
Karl-Heinz Dammer ◽  
Leifeng Guo ◽  
Zhen Cao
Keyword(s):  
Precision Agriculture ◽  
High Speed ◽  
Low Cost ◽  
Detection Algorithm ◽  
Aerial Images ◽  
Mountain Areas ◽  
Crop Monitoring ◽  
Proposed Model ◽  
Small Fields ◽  
Open Question

To date, unmanned aerial vehicles (UAVs), commonly known as drones, have been widely used in precision agriculture (PA) for crop monitoring and crop spraying, allowing farmers to increase the efficiency of the farming process, meanwhile reducing environmental impact. However, to spray pesticides effectively and safely to the trees in small fields or rugged environments, such as mountain areas, is still an open question. To bridge this gap, in this study, an onboard computer vision (CV) component for UAVs is developed. The system is low-cost, flexible, and energy-effective. It consists of two parts, the hardware part is an Intel Neural Compute Stick 2 (NCS2), and the software part is an object detection algorithm named the Ag-YOLO. The NCS2 is 18 grams in weight, 1.5 watts in energy consumption, and costs about $66. The proposed model Ag-YOLO is inspired by You Only Look Once (YOLO), trained and tested with aerial images of areca plantations, and shows high accuracy (F1 score = 0.9205) and high speed [36.5 frames per second (fps)] on the target hardware. Compared to YOLOv3-Tiny, Ag-YOLO is 2× faster while using 12× fewer parameters. Based on this study, crop monitoring and crop spraying can be synchronized into one process, so that smart and precise spraying can be performed.

Sign in / Sign up

Export Citation Format

Share Document