scholarly journals Fusarium Wilt of Radish Detection Using RGB and Near Infrared Images from Unmanned Aerial Vehicles

2020 ◽  
Vol 12 (17) ◽  
pp. 2863 ◽  
Author(s):  
L. Minh Dang ◽  
Hanxiang Wang ◽  
Yanfen Li ◽  
Kyungbok Min ◽  
Jin Tae Kwak ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fusarium Wilt ◽  
Near Infrared ◽  
Wilt Disease ◽  
Pollinator Decline ◽  
Main Research ◽  
Aerial Vehicles ◽  
Segmented Images ◽  
Rgb Images ◽  
Side Dishes

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.

Thermal and multispectral images from Unmanned Aerial Vehicles (UAVs) for water presence detection in temporary streams: first results

2021 ◽  
Author(s):  
Massimo Micieli ◽  
Gianluca Botter ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Near Infrared ◽  
Infrared Band ◽  
Temporary Streams ◽  
Water Index ◽  
Long Wavelength ◽  
Research Activities ◽  
Aerial Vehicles ◽  
Normalized Difference Water Index ◽  
First Results

<p>UAVs (Unmanned Aerial Vehicles) are increasingly used for monitoring river networks with a broad range of purposes. In this contribution, we focus on the use of multispectral sensors, either in the thermal infrared band LWIR (Long-wavelength infrared, 8-15 µm) or in the infrared band NIR (Near-infrared, 0.75-1.4 µm) to map network dynamics in temporary streams. Specifically, we discuss the first results of a set of surveys carried out in 2020 within a small river catchment located in northern Calabria (southern Italy), as part of the research activities of the ERC-funded DyNET project. Preliminary, a rigorous methodology was identified to perform on-site surveys and to process and analyse the acquired images. Experimental results show that the combined use of LWIR and NIR sensors is a suitable solution for detecting water presence in channels characterized by different hydraulic and morphologic conditions. LWIR sensors alone allow one to discriminate water presence only when the thermal contrast with the surrounding environment is high. On the other hand, NIR sensors permit to detect the presence of water in most of the analyzed settings through the estimate of the Normalized Difference Water Index (NDWI). However, NIR sensors can be misled in case of shallow water depth, due to the NIR radiation emitted by the riverbed merging with that of the water. Overall, the study demonstrates that a combined LWIR/NIR approach allows addressing a broader range of conditions. Moreover, the information provided can be further enhanced by combining it with geomorphologic information and basic hydraulic concepts.</p>

scholarly journals Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

2017 ◽  
Vol 11 (04) ◽  
pp. 1 ◽  
Author(s):  
Jin Gwan Ha ◽  
Hyeonjoon Moon ◽  
Jin Tae Kwak ◽  
Syed Ibrahim Hassan ◽  
Minh Dang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fusarium Wilt ◽  
Deep Convolutional Neural Network ◽  
Aerial Vehicles

scholarly journals Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2706 ◽  
Author(s):  
Waleed Ejaz ◽  
Muhammad Awais Azam ◽  
Salman Saadat ◽  
Farkhund Iqbal ◽  
Abdul Hanan
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Disaster Management ◽  
Future Research ◽  
Wildfire Management ◽  
Holistic View ◽  
Main Research ◽  
Research Areas ◽  
Aerial Vehicles ◽  
Iot Platform

Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.

scholarly journals Measuring Landscape Albedo Using Unmanned Aerial Vehicles

2018 ◽  
Vol 10 (11) ◽  
pp. 1812 ◽  
Author(s):  
Chang Cao ◽  
Xuhui Lee ◽  
Joseph Muhlhausen ◽  
Laurent Bonneau ◽  
Jiaping Xu
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Surface Albedo ◽  
Near Infrared ◽  
Surface Energy Balance ◽  
Landsat Image ◽  
Local Climate ◽  
Clear Sky ◽  
Aerial Vehicles ◽  
Albedo Change ◽  
Sky Conditions

Surface albedo is a critical parameter in surface energy balance, and albedo change is an important driver of changes in local climate. In this study, we developed a workflow for landscape albedo estimation using images acquired with a consumer-grade camera on board unmanned aerial vehicles (UAVs). Flight experiments were conducted at two sites in Connecticut, USA and the UAV-derived albedo was compared with the albedo obtained from a Landsat image acquired at about the same time as the UAV experiments. We find that the UAV estimate of the visibleband albedo of an urban playground (0.037 ± 0.063, mean ± standard deviation of pixel values) under clear sky conditions agrees reasonably well with the estimates based on the Landsat image (0.047 ± 0.012). However, because the cameras could only measure reflectance in three visible bands (blue, green, and red), the agreement is poor for shortwave albedo. We suggest that the deployment of a camera that is capable of detecting reflectance at a near-infrared waveband should improve the accuracy of the shortwave albedo estimation.

scholarly journals Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 2937
Author(s):  
Linglin Zeng ◽  
Guozhang Peng ◽  
Ran Meng ◽  
Jianguo Man ◽  
Weibo Li ◽  
...  
Keyword(s):  
Grain Yield ◽  
Unmanned Aerial Vehicles ◽  
Precision Agriculture ◽  
Nitrogen Fertilization ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Flowering Stage ◽  
Aerial Vehicles ◽  
Color Indices ◽  
Rgb Images

Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

scholarly journals RGB Spectral Indices for the Analysis of Soil Protection by Vegetation Cover against Erosive Processes

2020 ◽  
Author(s):  
Henry Antonio Pacheco Gil ◽  
Argenis de Jesús Montilla Pacheco
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Vegetation Cover ◽  
Near Infrared ◽  
Coastal Region ◽  
Soil Protection ◽  
Spectral Indices ◽  
Geospatial Technologies ◽  
Rgb Images ◽  
Airborne Sensors ◽  
Good Number

The vegetation cover plays a fundamental role in protecting the soil from erosive processes. Many researchers have developed investigations for the calculation of the RUSLE C Factor, with the use of operating bands in the near infrared. With the current advances in Geospatial Technologies, there are a good number of RGB airborne sensors in Unmanned Aerial Vehicles (UVA). The objective of this chapter is to evaluate some RGB indexes, proposed in the literature, for the protection of the soil from erosive processes by vegetation cover, in a region with a high agricultural vocation. The methodology consisted of capturing RGB images in an area of the Ecuadorian coastal region and calculating in thematic indices, within the visible one, which offer the possibility of quickly differentiating vegetation from other types of coverage on the ground. The evaluation allowed to define which indexes present the best results and adaptation to the type of crop or plant mass mapped, and to propose their use for zoning of risk of erosion under the agro-ecological conditions of the study area.

scholarly journals A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 40 ◽  
Author(s):  
Jayme Garcia Arnal Barbedo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Future Research ◽  
Research Gaps ◽  
Urban Settings ◽  
Main Research ◽  
Aerial Vehicles ◽  
The Subject ◽  
Imaging Sensors ◽  
Privacy Risks ◽  
Critical Overview

Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.

Detection of Stress Induced by Soybean Aphid (Hemiptera: Aphididae) Using Multispectral Imagery from Unmanned Aerial Vehicles

2019 ◽  
Vol 113 (2) ◽  
pp. 779-786 ◽  
Author(s):  
Zachary P D Marston ◽  
Theresa M Cira ◽  
Erin W Hodgson ◽  
Joseph F Knight ◽  
Ian V Macrae ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Open Field ◽  
Near Infrared ◽  
Field Trials ◽  
Soybean Aphid ◽  
Multispectral Imagery ◽  
Near Infrared Reflectance ◽  
Aerial Vehicles ◽  
Induced Stress ◽  
Aphid Populations

Abstract Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a common pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), in North America requiring frequent scouting as part of an integrated pest management plan. Current scouting methods are time consuming and provide incomplete coverage of soybean. Unmanned aerial vehicles (UAVs) are capable of collecting high-resolution imagery that offer more detailed coverage in agricultural fields than traditional scouting methods. Recently, it was documented that changes to the spectral reflectance of soybean canopies caused by aphid-induced stress could be detected from ground-based sensors; however, it remained unknown whether these changes could also be detected from UAV-based sensors. Small-plot trials were conducted in 2017 and 2018 where cages were used to manipulate aphid populations. Additional open-field trials were conducted in 2018 where insecticides were used to create a gradient of aphid pressure. Whole-plant soybean aphid densities were recorded along with UAV-based multispectral imagery. Simple linear regressions were used to determine whether UAV-based multispectral reflectance was associated with aphid populations. Our findings indicate that near-infrared reflectance decreased with increasing soybean aphid populations in caged trials when cumulative aphid days surpassed the economic injury level, and in open-field trials when soybean aphid populations were above the economic threshold. These findings provide the first documentation of soybean aphid-induced stress being detected from UAV-based multispectral imagery and advance the use of UAVs for remote scouting of soybean aphid and other field crop pests.

Method of Vegetation Detection Using RGB Images Made by Unmanned Aerial Vehicles on the Basis of Colour and Texture Analysis

2019 ◽  
pp. 55-62
Author(s):  
Michael Y. Kataev ◽  
Maria M. Dadonova
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Texture Analysis ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Digital Cameras ◽  
Vegetation Types ◽  
Aerial Vehicles ◽  
Rgb Images ◽  
Thematic Processing ◽  
Surface Vegetation

The article describes the capability of application of RGB?1 images made by digital cameras for detection of Earth surface (vegetation) types. A set of measures necessary to be taken for processing of the images made by unmanned aerial vehicles (UAV) in real-time is described. Application of analogue of vegetation index allows detecting vegetation on an RGB image, which increases the probability of correct detection of surface (vegetation) type. Methods of preliminary and thematic processing of images required for positive detection of surface types are considered. Texture analysis is applied for detection of vegetation type. The results of the processing of real images are provided.

Programmable Multispectral Imager Development as Light Weight Payload for Low Cost Fixed Wing Unmanned Aerial Vehicles

2009 ◽  
Author(s):  
Yiding Han ◽  
Austin Jensen ◽  
Huifang Dou
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Near Infrared ◽  
Low Cost ◽  
Measurement Unit ◽  
Light Weight ◽  
Aerial Vehicles ◽  
System Module ◽  
Multispectral Remote Sensing ◽  
Cost Efficient ◽  
Multispectral Imager

In this paper, we have developed a light-weight and cost-efficient multispectral imager payload for low cost fixed wing UAVs (Unmanned Aerial Vehicles) that need no runway for takeoff and landing. The imager is band-reconfigurable, covering both visual (RGB) and near infrared (NIR) spectrum. The number of the RGB and NIR sensors is scalable, depending on the demands of specific applications. The UAV on-board microcomputer programs and controls the imager system, synchronizing each camera individually to capture airborne imagery. It also bridges the payload to the UAV system by sending and receiving message packages. The airborne imagery is time-stamped with the corresponding local and geodetic coordinates data measured by the onboard IMU (Inertia Measurement Unit) and GPS (Global Positioning System) module. Subsequently, the imagery will be orthorectified with the recorded geo-referencing data. The application of such imager system includes multispectral remote sensing, ground mapping, target recognition, etc. In this paper, we will outline the technologies, demonstrate our experimental results from actual UAV flight missions, and compare the results with our previous imager system.

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