scholarly journals Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements

2021 ◽  
Vol 13 (22) ◽  
pp. 4560
Author(s):  
Lili Luo ◽  
Qingrui Chang ◽  
Qi Wang ◽  
Yong Huang
Keyword(s):  
Mosaic Virus ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Biochemical Parameter ◽  
Maize Dwarf Mosaic Virus ◽  
Support Vector ◽  
Classification Models ◽  
Linear Discriminant ◽  
And Control

Prompt monitoring of maize dwarf mosaic virus (MDMV) is critical for the prevention and control of disease and to ensure high crop yield and quality. Here, we first analyzed the spectral differences between MDMV-infected red leaves and healthy leaves and constructed a sensitive index (SI) for measurements. Next, based on the characteristic bands (Rλ) associated with leaf anthocyanins (Anth), we determined vegetation indices (VIs) commonly used in plant physiological and biochemical parameter inversion and established a vegetation index (VIc) by utilizing the combination of two arbitrary bands following the construction principles of NDVI, DVI, RVI, and SAVI. Furthermore, we developed classification models based on linear discriminant analysis (LDA) and support vector machine (SVM) in order to distinguish the red leaves from healthy leaves. Finally, we performed UR, MLR, PLSR, PCR, and SVM simulations on Anth based on Rλ, VIs, VIc, and Rλ + VIs + VIc and indirectly estimated the severity of MDMV infection based on the relationship between the reflection spectra and Anth. Distinct from those of the normal leaves, the spectra of red leaves showed strong reflectance characteristics at 640 nm, and SI increased with increasing Anth. Moreover, the accuracy of the two VIc-based classification models was 100%, which is significantly higher than that of the VIs and Rλ-based models. Among the Anth regression models, the accuracy of the MLR model based on Rλ + VIs + VIc was the highest (R2c = 0.85; R2v = 0.74). The developed models could accurately identify MDMV and estimate the severity of its infection, laying the theoretical foundation for large-scale remote sensing-based monitoring of this virus in the future.

scholarly journals Analysis of the Influence of Vegetation Index Choice on the Classification of Satellite Images for Monitoring Forest Pathology

2020 ◽  
pp. paper49-1-paper49-12
Author(s):  
Evgeniy Trubakov ◽  
Olga Trubakova
Keyword(s):  
Satellite Images ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Multispectral Images ◽  
Classification Methods ◽  
Planted Forests ◽  
The Earth ◽  
Evaluation Algorithm ◽  
And Control

Rational use of natural resources and control over their recovery, as well as over destruction due to natural and technogenic causes, is currently one of the most urgent problems of the humanity. Forests are no exception. Multispectral images from Earth’s satellites are most often used for monitoring changes in forest planting. This is due to the fact that merging images taken in certain spectra makes it possible to recognize vegetation containing chlorophyll quite well. It also allows to detect changes in the level of chlorophyll, which shows the differences between healthy and damaged plants. Large areas of planted forests create the need to process huge amounts of data, which is difficult to do manually. One of the most important stages of image processing is the classification of objects in these images. This paper deals with various classification methods used to solve the problem of classifying images of remote sensing of the Earth. As a result, it was decided to evaluate the accuracy of classification methods on various vegetation indices. In the course of the study, the evaluation algorithm was determined, as well as one of the options for analyzing the results obtained. Conclusions were made about the work of classification methods on different vegetation indices.

scholarly journals Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

2020 ◽  
Vol 10 (8) ◽  
pp. 2667 ◽  
Author(s):  
Xueting Wang ◽  
Sha Zhang ◽  
Lili Feng ◽  
Jiahua Zhang ◽  
Fan Deng
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Regression Equations ◽  
Summer Maize ◽  
Temporal Shift ◽  
Modis Evi ◽  
Multi Temporal ◽  
Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

An Efficient Pipeline for Crop Image Extraction and Vegetation Index Derivation Using Unmanned Aerial Systems

2020 ◽  
Vol 63 (4) ◽  
pp. 1133-1146
Author(s):  
Beichen Lyu ◽  
Stuart D. Smith ◽  
Yexiang Xue ◽  
Katy M. Rainey ◽  
Keith Cherkauer
Keyword(s):  
Data Processing ◽  
High Throughput ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Unmanned Aerial Systems ◽  
Growth Stages ◽  
Processing Pipeline ◽  
High Throughput Phenotyping ◽  
Aerial Systems

HighlightsThis study addresses two computational challenges in high-throughput phenotyping: scalability and efficiency.Specifically, we focus on extracting crop images and deriving vegetation indices using unmanned aerial systems.To this end, we outline a data processing pipeline, featuring a crop localization algorithm and trie data structure.We demonstrate the efficacy of our approach by computing large-scale and high-precision vegetation indices in a soybean breeding experiment, where we evaluate soybean growth under water inundation and temporal change.Abstract. In agronomy, high-throughput phenotyping (HTP) can provide key information for agronomists in genomic selection as well as farmers in yield prediction. Recently, HTP using unmanned aerial systems (UAS) has shown advantages in both cost and efficiency. However, scalability and efficiency have not been well studied when processing images in complex contexts, such as using multispectral cameras, and when images are collected during early and late growth stages. These challenges hamper further analysis to quantify phenotypic traits for large-scale and high-precision applications in plant breeding. To solve these challenges, our research team previously built a three-step data processing pipeline, which is highly modular. For this project, we present improvements to the previous pipeline to improve canopy segmentation and crop plot localization, leading to improved accuracy in crop image extraction. Furthermore, we propose a novel workflow based on a trie data structure to compute vegetation indices efficiently and with greater flexibility. For each of our proposed changes, we evaluate the advantages by comparison with previous models in the literature or by comparing processing results using both the original and improved pipelines. The improved pipeline is implemented as two MATLAB programs: Crop Image Extraction version 2 (CIE 2.0) and Vegetation Index Derivation version 1 (VID 1.0). Using CIE 2.0 and VID 1.0, we compute canopy coverage and normalized difference vegetation indices (NDVIs) for a soybean phenotyping experiment. We use canopy coverage to investigate excess water stress and NDVIs to evaluate temporal patterns across the soybean growth stages. Both experimental results compare favorably with previous studies, especially for approximation of soybean reproductive stage. Overall, the proposed methodology and implemented experiments provide a scalable and efficient paradigm for applying HTP with UAS to general plant breeding. Keywords: Data processing pipeline, High-throughput phenotyping, Image processing, Soybean breeding, Unmanned aerial systems, Vegetation indices.

scholarly journals Estimating the Threshold of Detection on Tree Crown Defoliation Using Vegetation Indices from UAS Multispectral Imagery

Drones ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 80 ◽  
Author(s):  
Kaori Otsu ◽  
Magda Pla ◽  
Andrea Duane ◽  
Adrián Cardil ◽  
Lluís Brotons
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thaumetopoea Pityocampa ◽  
Multispectral Imagery ◽  
Mixed Stands ◽  
Crown Defoliation ◽  
Total Study Area ◽  
Pine Species

Periodical outbreaks of Thaumetopoea pityocampa feeding on pine needles may pose a threat to Mediterranean coniferous forests by causing severe tree defoliation, growth reduction, and eventually mortality. To cost–effectively monitor the temporal and spatial damages in pine–oak mixed stands using unmanned aerial systems (UASs) for multispectral imagery, we aimed at developing a simple thresholding classification tool for forest practitioners as an alternative method to complex classifiers such as Random Forest. The UAS flights were performed during winter 2017–2018 over four study areas in Catalonia, northeastern Spain. To detect defoliation and further distinguish pine species, we conducted nested histogram thresholding analyses with four UAS-derived vegetation indices (VIs) and evaluated classification accuracy. The normalized difference vegetation index (NDVI) and NDVI red edge performed the best for detecting defoliation with an overall accuracy of 95% in the total study area. For discriminating pine species, accuracy results of 93–96% were only achievable with green NDVI in the partial study area, where the Random Forest classification combined for defoliation and tree species resulted in 91–93%. Finally, we achieved to estimate the average thresholds of VIs for detecting defoliation over the total area, which may be applicable across similar Mediterranean pine stands for monitoring regional forest health on a large scale.

scholarly journals Synthetic Aperture Radar (L band) and Optical Vegetation Indices for Discriminating the Brazilian Savanna Physiognomies: A Comparative Analysis

2005 ◽  
Vol 9 (15) ◽  
pp. 1-15 ◽  
Author(s):  
Edson E. Sano ◽  
Laerte G. Ferreira ◽  
Alfredo R. Huete
Keyword(s):  
Comparative Analysis ◽  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Primary Response ◽  
Synthetic Aperture ◽  
L Band ◽  
Vegetation Structures ◽  
Aperture Radar

Abstract The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados’ physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Data Mining Techniques for Analysing Employment Data

2019 ◽  
Vol 9 (2) ◽  
pp. 555-556
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Classification Tree ◽  
Predictive Modelling ◽  
Testing Procedure ◽  
Support Vector ◽  
Linear Discriminant ◽  
Rigorous Testing ◽  
Data Partitions ◽  
Binary Classifiers

This paper proposes a methodology that uses a large-scale employment dataset in order to explore which factors affect employment and how. The proposed methodology is a combination of predictive modelling, variable significance analysis, and VEC analysis. Modelling is based on logistic regression, linear discriminant analysis, neural network, classification tree, and support vector machine. Following the CRISP-DM standard process model, we train binary classifiers optimising their hyper-parameters and measure their performance by prediction accuracy, ROC analysis, and AUC. Using sensitivity analysis, we rank the variable significance in order to identify and measure factors of employment. Using VEC analysis, we further explore how values of those factors affect employment. Findings show that best performing models are neural networks and support vector machines with preference to the latter for quality of VEC. Experiments also suggest that education and age are primary contributors for correct classification with specific value distribution, discussed in the paper. All results were validated using a rigorous testing procedure that involves training, validation, and test data partitions and a combination of multiple runs along with three-fold cross-validation. This study addresses some gaps in previous research publications, which lack quantification of the conclusions made.

scholarly journals Classification models of structure - P-glycoprotein activity of drugs

2016 ◽  
Vol 62 (2) ◽  
pp. 173-179
Author(s):  
V.Yu. Grigorev ◽  
S.L. Solodova ◽  
D.E. Polianczyk ◽  
O.A. Raevsky
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
External Validation ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Classification Models ◽  
Qsar Modeling ◽  
Linear Discriminant ◽  
P Glycoprotein ◽  
Dragon Descriptors

Thirty three classification models of substrate specificity of 177 drugs to P-glycoprotein have been created using of the linear discriminant analysis, random forest and support vector machine methods. QSAR modeling was carried out using 2 strategies. The first strategy consisted in search of all possible combinations from 1¸5 descriptors on the basis of 7 most significant molecular descriptors with clear physico-chemical interpretation. In the second case forward selection procedure up to 5 descriptors, starting from the best single descriptor was used. This strategy was applied to a set of 387 DRAGON descriptors. It was found that only one of 33 models has necessary statistical parameters. This model was designed by means of the linear discriminant analysis on the basis of a single descriptor of H-bond (SCad). The model has good statistical characteristics as evidenced by results to both internal cross-validation, and external validation with application of 44 new chemicals. This confirms an important role of hydrogen bond in the processes connected with penetration of chemical compounds through a blood-brain barrier

scholarly journals MAIZE YIELD ESTIMATION IN KENYA USING MODIS

2020 ◽  
Vol V-3-2020 ◽  
pp. 477-482
Author(s):  
B. K. Kenduiywo ◽  
A. Ghosh ◽  
R. Hijmans ◽  
L. Ndungu
Keyword(s):  
Crop Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Research ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Maize Yield ◽  
Percentage Error ◽  
Support Vector ◽  
County Level

Abstract. Monitoring staple crop production can support agricultural research, business such as crop insurance, and government policy. Obtaining accurate estimates through field work is very expensive, and estimating it through remote sensing is promising. We estimated county-level maize yield for the 37 maize producing countries in Kenya from 2010 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Support Vector Regression (SVR) and Random Forest (RF) were used to fit models with observed county level maize yield as a function of vegetation indices. The following five MODIS vegetation indices were used: green normalized difference vegetation index, normalized difference vegetation index, normalized difference moisture index, gross primary production, and fraction of photosynthetically active radiation. The models were evaluated with 5-fold leave one year out cross-validation. For SVR, R2 was 0.70, the Root Mean Square Error (RMSE) was 0.50 MT/ha and Mean Absolute Percentage Error (MAPE) was 27.6%. On the other hand for RF these were 0.69, 0.51 MT/ha and 29.3% respectively. These results are promising and should be tested in specific applications to understand if they are good enough for use.

scholarly journals Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

OENO One ◽  
2020 ◽  
Vol 54 (2) ◽  
pp. 189-197 ◽  
Author(s):  
Marco Sozzi ◽  
Ahmed Kayad ◽  
Francesco Marinello ◽  
James Taylor ◽  
Bruno Tisseyre
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Significance Test ◽  
Support Vector ◽  
Field Scale ◽  
Mixed Pixel ◽  
Normalised Difference Vegetation Index ◽  
Aerial Vehicle ◽  
Sentinel 2

Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.

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