scholarly journals Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10701
Author(s):  
Rhushalshafira Rosle ◽  
Nik Norasma Che’Ya ◽  
Yuhao Ang ◽  
Fariq Rahmat ◽  
Aimrun Wayayok ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Weed Management ◽  
Agricultural Development ◽  
Automated Detection ◽  
Rice Fields ◽  
Weed Detection ◽  
Remote Sensing Technique ◽  
Sensing Platforms ◽  
Remote Sensing Techniques ◽  
The Ideal

This paper reviewed the weed problems in agriculture and how remote sensing techniques can detect weeds in rice fields. The comparison of weed detection between traditional practices and automated detection using remote sensing platforms is discussed. The ideal stage for controlling weeds in rice fields was highlighted, and the types of weeds usually found in paddy fields were listed. This paper will discuss weed detection using remote sensing techniques, and algorithms commonly used to differentiate them from crops are deliberated. However, weed detection in rice fields using remote sensing platforms is still in its early stages; weed detection in other crops is also discussed. Results show that machine learning (ML) and deep learning (DL) remote sensing techniques have successfully produced a high accuracy map for detecting weeds in crops using RS platforms. Therefore, this technology positively impacts weed management in many aspects, especially in terms of the economic perspective. The implementation of this technology into agricultural development could be extended further.

scholarly journals Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1809
Author(s):  
Muhammad Huzaifah Mohd Roslim ◽  
Abdul Shukor Juraimi ◽  
Nik Norasma Che’Ya ◽  
Nursyazyla Sulaiman ◽  
Muhammad Noor Hazwan Abd Manaf ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Weed Management ◽  
Management Practices ◽  
Crop Yields ◽  
Supply And Demand ◽  
Learning Approaches ◽  
Future Challenges ◽  
Aerial Vehicle ◽  
Remote Sensing Systems ◽  
Remote Sensing Techniques

Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.

scholarly journals Investigation of the Effect of Electromagnetic Radiation on Human Health Using Remote Sensing Technique

2021 ◽  
Vol 11 (1) ◽  
pp. 117-122
Author(s):  
Mohammed Mejbel Salih
Keyword(s):  
Remote Sensing ◽  
Electromagnetic Radiation ◽  
Human Health ◽  
Mobile Phones ◽  
Social Life ◽  
Measuring Device ◽  
Remote Sensing Technique ◽  
Negative Side Effects ◽  
Remote Sensing Techniques ◽  
Effects Of Radiation

In the previous two decades, there has been a rapid and remarkable development in the field of communication technologies to encompass many joints of social life, especially devices for daily use, from mobile phones to laptops, to microwave transmitting and receiving towers, in addition to electromagnetic induction furnaces. This puts us in the fact that we are currently inside a multi-spectrum electromagnetic cloud. In this research, the effect of exposure to electromagnetic radiation and checking the negative side effects on the human body was studied through the use of remote sensing techniques, an electromagnetic radiation intensity measuring device for some devices circulating daily with humans, i.e. mobile phones, to assess the effect of this radiation emitted on human health. The study adopts elementary standards to determine the value of the radioactive energy and its effect on human organs after taking samples from cell phones. In addition, the results show that the effects of radiation depend on depends on the time of exposure.

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 REMOTE SENSING TECHNOLOGIES FOR LINEAR INFRASTRUCTURE MONITORING

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 461-468 ◽  
Author(s):  
P. D’Aranno ◽  
A. Di Benedetto ◽  
M. Fiani ◽  
M. Marsella
Keyword(s):  
Remote Sensing ◽  
Structural Characteristics ◽  
Methodological Approach ◽  
Laser Scanner ◽  
3D Models ◽  
Remote Sensing Technique ◽  
Safety Condition ◽  
Dense Point ◽  
Remote Sensing Techniques ◽  
Monitoring Technologies

<p><strong>Abstract.</strong> The need for a continuous evaluation of the state of preservation of civil infrastructures during their lifetime is increasingly requiring advanced monitoring technologies. The improvement of spatial and temporal resolution of the measurements is now one of the most significant achievement, especially for large infrastructures. Monitoring actions are necessary to maintain safety conditions by controlling the evolution of deformation patterns or detecting significant instabilities. Remote sensing technique such as Differential Interferometry by Synthetic Aperture Radar (DInSAR) allows identifying environmental vulnerability and potential damages on large road infrastructures thus contributing to plan and optimize maintenance actions. DInSAR data allow to highlight instability processes and to quantify mean deformation velocities and displacement time series. This information can be analysed considering geotechnical and structural characteristics and adopted to evaluate possible safety condition improvement and damage mitigation. Using proximal remote sensing techniques, such as Light Detection And Ranging (LiDAR), it is possible to analyse the pavement conditions on 3D models derived from a dense point cloud acquired by Mobile Laser Scanner (MLS). By combining the DInSAR and LiDAR datasets a great improvement is expected in the capability to promptly identifying critical situations and understanding potential risks affecting extended road infrastructures. The principal aim of this paper is to provide a general overview of the most innovative remote sensing techniques for infrastructure safety condition assessments. Furthermore, a methodological approach to define a reliable procedure for data processing and integration is applied on a test area located in the municipality of Rome.</p>

scholarly journals Spectral Band Optical Analysis for Shape and Material of Terrestrial Imaging by Using Remote Sensing Technique

2018 ◽  
Vol 3 (5) ◽  
pp. 52
Author(s):  
Safaa Mustafa Hameed ◽  
Abdulrazak A. S. Mohammed
Keyword(s):  
Remote Sensing ◽  
Spectral Band ◽  
Satellite Image ◽  
Processing System ◽  
Physical Test ◽  
Remote Sensing Technique ◽  
Gate Structure ◽  
Ancient City ◽  
Remote Sensing Techniques ◽  
Transmission And Absorption

Photogrammetric grid is the generation of processing system (which is one of the remote sensing techniques) with efficiency developed based on imaging and computer cluster parallel processing; the new application of photogrammetric technique has been applied on the Castel-Gate of Erbil city. It is a way of analyzing the objects especially for a traditional one "the castle in the ancient city of Erbil". This paper got the analysis of Erbil Castel-Gate structure using visible & IR spectral band analysis for transmission and absorption energy (El ) based on civil study, as well as the analyzing of chemical & physical test. On other hand the Satellite image of castle has been analyzed by using photogrammetric & GIS techniques. The result calculated the reflected energy values. 

scholarly journals Geophysical studies of floating ice by remote sensing

1975 ◽  
Vol 15 (73) ◽  
pp. 305-328 ◽  
Author(s):  
W. J. Campbell ◽  
W. F. Weeks ◽  
R. O. Ramseier ◽  
P. Gloersen
Keyword(s):  
Remote Sensing ◽  
Rapid Evolution ◽  
Passive Microwave ◽  
Scientific Interest ◽  
Current Increase ◽  
Ice Roughness ◽  
Sensing Platforms ◽  
Ice Drift ◽  
Remote Sensing Techniques

AbstractThis paper presents an overview of recent remote-sensing techniques as applied to geophysical studies of floating ice. The current increase in scientific interest in floating ice has occurred during a time of rapid evolution of both remote-sensing platforms and sensors. Mesoscale and macroscale studies of floating ice are discussed under three sensor categories: visual, passive microwave, and active microwave. The specific studies that are reviewed primarily investigate ice drift and deformation, and ice type and ice roughness identification and distribution.

scholarly journals Geophysical studies of floating ice by remote sensing

1975 ◽  
Vol 15 (73) ◽  
pp. 305-328 ◽  
Author(s):  
W. J. Campbell ◽  
W. F. Weeks ◽  
R. O. Ramseier ◽  
P. Gloersen
Keyword(s):  
Remote Sensing ◽  
Rapid Evolution ◽  
Passive Microwave ◽  
Scientific Interest ◽  
Current Increase ◽  
Ice Roughness ◽  
Sensing Platforms ◽  
Ice Drift ◽  
Remote Sensing Techniques

AbstractThis paper presents an overview of recent remote-sensing techniques as applied to geophysical studies of floating ice. The current increase in scientific interest in floating ice has occurred during a time of rapid evolution of both remote-sensing platforms and sensors. Mesoscale and macroscale studies of floating ice are discussed under three sensor categories: visual, passive microwave, and active microwave. The specific studies that are reviewed primarily investigate ice drift and deformation, and ice type and ice roughness identification and distribution.

Detection of Weed Species in Soybean Using Multispectral Digital Images

2004 ◽  
Vol 18 (3) ◽  
pp. 742-749 ◽  
Author(s):  
Kevin D. Gibson ◽  
Richard Dirks ◽  
Case R. Medlin ◽  
Loree Johnston
Keyword(s):  
Remote Sensing ◽  
Weed Management ◽  
Digital Images ◽  
Management Systems ◽  
Bare Ground ◽  
Weed Species ◽  
Weed Detection ◽  
Giant Foxtail ◽  
Soybean Canopy

The objective of this research was to assess the accuracy of remote sensing for detecting weed species in soybean based on two primary criteria: the presence or absence of weeds and the identification of individual weed species. Treatments included weeds (giant foxtail and velvetleaf) grown in monoculture or interseeded with soybean, bare ground, and weed-free soybean. Aerial multispectral digital images were collected at or near soybean canopy closure from two field sites in 2001. Weedy pixels (1.3 m2) were separated from weed-free soybean and bare ground with no more than 11% error, depending on the site. However, the classification of weed species varied between sites. At one site, velvetleaf and giant foxtail were classified with no more than 17% error, when monoculture and interseeded plots were combined. However, classification errors were as high as 39% for velvetleaf and 17% for giant foxtail at the other site. Our results support the idea that remote sensing has potential for weed detection in soybean, particularly when weed management systems do not require differentiation among weed species. Additional research is needed to characterize the effect of weed density or cover and crop–weed phenology on classification accuracies.

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