Oat crown rust disease severity estimated at many time points using multispectral aerial photos

2021 ◽  
Author(s):  
Ian McNish ◽  
Kevin P. Smith
Keyword(s):  
Random Forest ◽  
Disease Severity ◽  
Unmanned Aerial Vehicle ◽  
Spectral Properties ◽  
Crown Rust ◽  
Spectral Signature ◽  
Growth Stages ◽  
Aerial Photos ◽  
Time Points ◽  
Aerial Vehicle

All plant breeding programs are dependent on plant phenotypic and genotypic data, but the development of phenotyping technology has been slow relative to that of genotyping. Crown rust (Puccinia coronata f. sp. avenae Erikss.) is the most important disease of cultivated oat (Avena sativa L.) making the development of disease resistant oat cultivars an important breeding objective. Visual observation is the most common scoring method, but it can be laborious and subjective. We visually scored a diverse collection of 256 oat lines at a total of twenty-seven time points in three disease nursery environments. Multispectral aerial photos were collected using an unmanned aerial vehicle at the same time points as the visual observations. The photos were analyzed and a subset of spectral properties of each plot were measured. Random forest modeling was used to model the relationship between the spectral properties of the plots and visually observed disease severity. The ability of the photo data and the random forest model to estimate visually observed disease severity was evaluated using three different cross-validation analyses. We specifically address the issue of assessing phenotyping accuracy across and within time points. The accuracy of the photo estimates was greatest for adult plants shortly before they began to senesce. Accuracy outside of that time frame is generally low, but statistically significant. Unmanned aerial vehicle mounted sensors could increase disease scoring efficiency, but additional investigation into the spectral signature of disease severity at all plant growth stages may be necessary to automate accurate full-season measurements.

scholarly journals Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 397
Author(s):  
Riccardo Dainelli ◽  
Piero Toscano ◽  
Salvatore Filippo Di Gennaro ◽  
Alessandro Matese
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Sustainable Management ◽  
Large Scale ◽  
Spectral Signature ◽  
Accurate Information ◽  
Data Interoperability ◽  
Critical Issues ◽  
Management Context ◽  
Aerial Vehicle

Forest sustainable management aims to maintain the income of woody goods for companies, together with preserving non-productive functions as a benefit for the community. Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable management. The use of UAV (Unmanned Aerial Vehicle) in precision forestry has exponentially increased in recent years, as demonstrated by more than 600 references published from 2018 until mid-2020 that were found in the Web of Science database by searching for “UAV”+“forest”. This result is even more surprising when compared with similar research for “UAV”+“agriculture”, from which emerge about 470 references. This shows how UAV–RS research forestry is gaining increasing popularity. In Part II of this review, analyzing the main findings of the reviewed papers (227), numerous strengths emerge concerning research technical issues. UAV–RS is fully applicated for obtaining accurate information from practical parameters (height, diameter at breast height (DBH), and biomass). Research effectiveness and soundness demonstrate that UAV–RS is now ready to be applied in a real management context. Some critical issues and barriers in transferring research products are also evident, namely,(1) hyperspectral sensors are poorly used, and their novel applications should be based on the capability of acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible and open-source tools that can foster researcher activities and support technology transfer among all forestry stakeholders, and (3) a clear lack exist in sensors and platforms interoperability for large-scale applications and for enabling data interoperability.

Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)

2020 ◽  
Vol 176 ◽  
pp. 105665
Author(s):  
Mahendra Bhandari ◽  
Amir M.H. Ibrahim ◽  
Qingwu Xue ◽  
Jinha Jung ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Disease Severity ◽  
Unmanned Aerial Vehicle ◽  
Aerial Imagery ◽  
Aerial Vehicle

scholarly journals New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (24) ◽  
pp. 4170
Author(s):  
Pengfei Chen ◽  
Fangyong Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Green Band ◽  
Validation Data ◽  
Texture Parameters ◽  
Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

Integration of the Target Position Correction Software with the High Endurance Quadcopter for Search and Rescue Mission

2015 ◽  
Vol 764-765 ◽  
pp. 713-717 ◽  
Author(s):  
Jie Tong Zou ◽  
Zheng Yan Pan ◽  
Dong Lin Zhang ◽  
Rui Feng Zheng
Keyword(s):  
Air Pollution ◽  
Unmanned Aerial Vehicle ◽  
Target Position ◽  
Search And Rescue ◽  
Pollution Monitoring ◽  
Rescue Mission ◽  
Aerial Photos ◽  
Air Pollution Monitoring ◽  
Position Correction ◽  
Aerial Vehicle

An Unmanned Aerial vehicle (UAV), commonly known as drone, is an aircraft without a human pilot aboard. In recent years, some UAVs are deployed for civil applications, such as policing, firefighting, air pollution monitoring, and aerial search and rescue, etc. Unmanned Aerial vehicle generally include fixed-wing and multi-rotor aircrafts. This research had developed a high endurance quadcopter for search and rescue mission. Target position correction software also was developed in this research. This program uses GPS coordinates embedded in the EXIF information of the aerial photos and more accurately calculates the target’s position. The target’s position error is less than ten meters in 75 meters altitude. Keywords: target position correction software, search and rescue, UAV, quadcopter, and GPS.

scholarly journals Analysis Of Aerial Photography With Drone Type Fixed Wing In Kotabaru, Lampung

2018 ◽  
Vol 2 (1) ◽  
pp. 102-107
Author(s):  
Indreswari Suroso ◽  
Erwhin Irmawan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Photography ◽  
Ground Level ◽  
Aerial Photographs ◽  
Aerial Photos ◽  
Above Ground Level ◽  
The World ◽  
Air Capture ◽  
Aerial Vehicle ◽  
Operational Time

In the world of photography is very closely related to the unmanned aerial vehicle called drones. Drones mounted camera so that the plane is pilot controlled from the mainland. Photography results were seen by the pilot after the drone aircraft landed. Drones are unmanned drones that are controlled remotely. Unmanned Aerial Vehicle (UAV), is a flying machine that operates with remote control by the pilot. Methode for this research are preparation assembly of drone, planning altitude flying, testing on ground, camera of calibration, air capture, result of aerial photos and analysis of result aerial photos. There are two types of drones, multicopter and fixed wing. Fixed wing  has an airplane like shape with a wing system. Fixed wing use bettery 4000 mAh . Fixed wing drone in this research used   mapping in  This drone has a load ability of 1 kg and operational time is used approximately 30 minutes for an areas 20 to 50 hectares with a height of 100 m  to 200 m and payload 1 kg  above ground level. The aerial photographs in Kotabaru produce excellent aerial photographs that can help mapping the local government in the Kotabaru region.

scholarly journals Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 957 ◽  
Author(s):  
Hengbiao Zheng ◽  
Jifeng Ma ◽  
Meng Zhou ◽  
Dong Li ◽  
Xia Yao ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Spectral Information ◽  
Growth Stages ◽  
Nitrogen Accumulation ◽  
Multispectral Imagery ◽  
Plant Nitrogen ◽  
N Nutrition ◽  
Aerial Vehicle

This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

GRAPHICAL PART OF LAND AND BUILDINGS REGISTRY BASED ON AERIAL PHOTOS FROM THE BOARD OF UNMANNED AERIAL VEHICLE (UAV)

2017 ◽  
Vol 165 (45) ◽  
pp. 36-48
Author(s):  
ARTUR PLICHTA ◽  
MICHAŁ WYCZAŁEK ◽  
IRENEUSZ WYCZAŁEK
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Basic Problem ◽  
Limited Extent ◽  
Geometrical Aspect ◽  
Aerial Photos ◽  
Registration Data ◽  
Accuracy Condition ◽  
Real Objects ◽  
Aerial Vehicle ◽  
Low Altitude

The paper attempts to develop a new way of verifying and updating data collected in Land and Property Databases, containing information on land and buildings. The report examines currently existing law regulating for the collection of registration of data, mainly in their geometrical aspect, proposes possible ways of validating these data and enriches with some new elements based on UAS technology. By supplementing the databases with new Land and Property objects the study was prepared, taking into account some new legislative provisions related to the principles and scope of the collected data in the Land and Property's resources. The basic problem with the use of photogrammetry from the UAV level for measuring the location and shape of considered objects is ensuring the proper accuracy. The compliance of accuracy condition and the visibility of the objects makes it possible to significantly supplement the registration data databases with some new elements such as terraces, verandas, stairs, etc. The paper discusses these issues and presents the results performed on real objects, together with their accuracy rating. It has been found that images made from low altitude can be used to measure new object classes, update land and buildings database, and also, to a limited extent, validate Land and Property Databases for another, from the up-visible objects.

Estimation of Corn Emergence Date Using UAV Imagery

2021 ◽  
Vol 64 (4) ◽  
pp. 1173-1183
Author(s):  
Chin Nee Vong ◽  
Stirling A. Stewart ◽  
Jianfeng Zhou ◽  
Newell R. Kitchen ◽  
Kenneth A. Sudduth
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Unmanned Aerial Vehicle ◽  
Classification Accuracy ◽  
Image Features ◽  
Shape Features ◽  
Size And Shape ◽  
Aerial Vehicle ◽  
Corn Plants ◽  
Emergence Date

HighlightsUAV imagery can be used to characterize newly-emerged corn plants.Size and shape features used in a random forest model are able to predict days after emergence within a 3-day window.Diameter and area were important size features for predicting DAE for the first, second, and third week of emergence.Abstract. Assessing corn (Zea mays L.) emergence uniformity soon after planting is important for relating to grain production and making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been used for determining corn densities at vegetative growth stage 2 (V2) and later, but not as a tool for quantifying emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery and a machine learning method. A field experiment was designed with four planting depths to obtain a range of corn emergence dates. UAV imagery was collected during the first, second, and third weeks after emergence. Acquisition height was approximately 5 m above ground level, which resulted in a ground sampling distance of 1.5 mm pixel-1. Seedling size and shape features derived from UAV imagery were used for DAE classification based on a random forest machine learning model. Results showed that 1-day DAE could be distinguished based on image features within the first week after initial corn emergence with a moderate overall classification accuracy of 0.49. However, for the second week and beyond, the overall classification accuracy diminished (0.20 to 0.35). When estimating DAE within a 3-day window (-1 to +1 day), the overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and the ratio of major axis length to area were important image features to predict corn DAE. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date to assist in assessing emergence uniformity. Additional studies are needed for fine-tuning the image collection procedures and image feature identification to improve accuracy. Keywords: Corn emergence, Image features, Random forest, Unmanned aerial vehicle.

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