Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

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.

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Performance Evaluation of Thermal Soaring Unmanned Aerial Vehicle for Coastal Surveillance in South Australia

50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition ◽

10.2514/6.2012-1182 ◽

2012 ◽

Author(s):

Elise Fahy ◽

Maziar Arjomandi

Keyword(s):

Performance Evaluation ◽

Unmanned Aerial Vehicle ◽

South Australia ◽

Aerial Vehicle

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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

Remote Sensing ◽

10.3390/rs12060957 ◽

2020 ◽

Vol 12 (6) ◽

pp. 957 ◽

Cited By ~ 2

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.

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Unmanned Aerial Vehicle Relay System: Performance Evaluation and 3D Location Optimization

IEEE Access ◽

10.1109/access.2020.2986182 ◽

2020 ◽

Vol 8 ◽

pp. 67635-67645

Author(s):

Sidqy I. Alnagar ◽

Anas M. Salhab ◽

Salam A. Zummo

Keyword(s):

Performance Evaluation ◽

Unmanned Aerial Vehicle ◽

System Performance ◽

Relay System ◽

Location Optimization ◽

Aerial Vehicle ◽

System Performance Evaluation

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Performance evaluation of Unmanned Aerial Vehicle ad hoc networks

SoutheastCon 2015 ◽

10.1109/secon.2015.7133020 ◽

2015 ◽

Cited By ~ 5

Author(s):

Robin Grodi ◽

Danda B. Rawat ◽

Chandra Bajracharya

Keyword(s):

Performance Evaluation ◽

Ad Hoc Networks ◽

Unmanned Aerial Vehicle ◽

Ad Hoc ◽

Vehicle Ad Hoc Networks ◽

Aerial Vehicle ◽

Hoc Networks

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

Phytopathology ◽

10.1094/phyto-09-20-0442-r ◽

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.

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Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

10.1101/369884 ◽

2018 ◽

Author(s):

Xiaqing Wang ◽

Ruyang Zhang ◽

Liang Han ◽

Hao Yang ◽

Wei Song ◽

...

Keyword(s):

Plant Height ◽

Unmanned Aerial Vehicle ◽

High Throughput ◽

Genome Wide Association Study ◽

Early Stage ◽

Growth Stages ◽

Tropical Maize ◽

Growth State ◽

Aerial Vehicle ◽

Trait Locus

AbstractPlant height is the key factor for plant architecture, biomass and yield in maize (Zea mays). In this study, plant height was investigated using unmanned aerial vehicle high-throughput phenotypic platforms (UAV-HTPPs) for maize diversity inbred lines at four important growth stages. Using an automated pipeline, we extracted accurate plant heights. We found that in temperate regions, from sowing to the jointing period, the growth rate for temperate maize was faster than tropical maize. However, from jointing to flowering stage, tropical maize maintained a vigorous growth state, and finally resulted in a taller plant than temperate lines. Genome-wide association study for temperate, tropical and both groups identified a total of 238 quantitative trait locus (QTLs) for the 16 plant height related traits over four growth periods. And, we found that plant height at different stages were controlled by different genes, for example, PIN1 controlled plant height at the early stage and PIN11 at the flowering stages. In this study, the plant height data collected by the UAV-HTTPs were credible and the genetic mapping power is high, indicating that the application of this UAV-HTTPs into the study of plant height will have great prospects.HighlightWe used UAV-based sensing platform to investigate plant height over 4 growth stages for different maize populations, and detected numbers of reliable QTLs using GWAS.

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Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data

Frontiers in Plant Science ◽

10.3389/fpls.2021.730181 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shuaipeng Fei ◽

Muhammad Adeel Hassan ◽

Yuntao Ma ◽

Meiyan Shu ◽

Qian Cheng ◽

...

Keyword(s):

Root Mean Square Error ◽

Winter Wheat ◽

Mean Square Error ◽

Unmanned Aerial Vehicle ◽

Grain Filling ◽

Growth Stages ◽

Yield Prediction ◽

Mean Square ◽

Entropy Weight ◽

Aerial Vehicle

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R2) = 0.527–0.667] compared to using a single multispectral sensor (R2 = 0.130–0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R2 = 0.667, root mean square error (RMSE) = 0.881 t ha–1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha–1. The EWF model outperformed at all the individual growth stages with R2 varying from 0.677 to 0.729. The best prediction result (R2 = 0.729, RMSE = 0.831 t ha–1, RRMSE = 14.3%, and MAE = 0.684 t ha–1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

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