scholarly journals Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Lukas Roth ◽  
Moritz Camenzind ◽  
Helge Aasen ◽  
Lukas Kronenberg ◽  
Christoph Barendregt ◽  
...  
Keyword(s):  
High Efficiency ◽  
Stem Elongation ◽  
Ground Cover ◽  
Support Vector ◽  
Multiview Images ◽  
Standard Tool ◽  
Plant Phenomics ◽  
High Throughput Phenotyping ◽  
Rgb Images ◽  
Plant Emergence

Early generation breeding nurseries with thousands of genotypes in single-row plots are well suited to capitalize on high throughput phenotyping. Nevertheless, methods to monitor the intrinsically hard-to-phenotype early development of wheat are yet rare. We aimed to develop proxy measures for the rate of plant emergence, the number of tillers, and the beginning of stem elongation using drone-based imagery. We used RGB images (ground sampling distance of 3 mm pixel-1) acquired by repeated flights (≥ 2 flights per week) to quantify temporal changes of visible leaf area. To exploit the information contained in the multitude of viewing angles within the RGB images, we processed them to multiview ground cover images showing plant pixel fractions. Based on these images, we trained a support vector machine for the beginning of stem elongation (GS30). Using the GS30 as key point, we subsequently extracted plant and tiller counts using a watershed algorithm and growth modeling, respectively. Our results show that determination coefficients of predictions are moderate for plant count (R2=0.52), but strong for tiller count (R2=0.86) and GS30 (R2=0.77). Heritabilities are superior to manual measurements for plant count and tiller count, but inferior for GS30 measurements. Increasing the selection intensity due to throughput may overcome this limitation. Multiview image traits can replace hand measurements with high efficiency (85–223%). We therefore conclude that multiview images have a high potential to become a standard tool in plant phenomics.

scholarly journals Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
David M. Deery ◽  
David J. Smith ◽  
Robert Davy ◽  
Jose A. Jimenez-Berni ◽  
Greg J. Rebetzke ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Reliability ◽  
Stem Elongation ◽  
Digital Camera ◽  
Ground Cover ◽  
Growth Stages ◽  
Rgb Images ◽  
High Degree ◽  
Pearson Correlations

Canopy ground cover (GC) is an important agronomic measure for evaluating crop establishment and early growth. This study evaluates the reliability of GC estimates, in the presence of varying light and dew on leaves, from three different ground-based sensors: (1) normalized difference vegetation index (NDVI) from the commercially available GreenSeeker®; (2) RGB images from a digital camera, where GC was determined as the portion of pixels from each image meeting a greenness criterion (i.e., Green−Red/Green+Red>0); and (3) LiDAR using two separate approaches: (a) GC from LiDAR red reflectance (whereby red reflectance less than five was classified as vegetation) and (b) GC from LiDAR height (whereby height greater than 10 cm was classified as vegetation). Hourly measurements were made early in the season at two different growth stages (tillering and stem elongation), among wheat genotypes highly diverse for canopy characteristics. The active NDVI showed the least variation through time and was particularly stable, regardless of the available light or the presence of dew. In addition, between-sample-time Pearson correlations for NDVI were consistently high and significant (P<0.0001), ranging from 0.89 to 0.98. In comparison, GC from LiDAR and RGB showed greater variation across sampling times, and LiDAR red reflectance was strongly influenced by the presence of dew. Excluding times when the light was exceedingly low, correlations between GC from RGB and NDVI were consistently high (ranging from 0.79 to 0.92). The high reliability of the active NDVI sensor potentially affords a high degree of flexibility for users by enabling sampling across a broad range of acceptable light conditions.

scholarly journals Regional Zenith Tropospheric Delay Modeling Based on Least Squares Support Vector Machine Using GNSS and ERA5 Data

2021 ◽  
Vol 13 (5) ◽  
pp. 1004
Author(s):  
Song Li ◽  
Tianhe Xu ◽  
Nan Jiang ◽  
Honglei Yang ◽  
Shuaimin Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
High Efficiency ◽  
Rapid Development ◽  
Support Vector ◽  
Tropospheric Delay ◽  
Improvement Rate ◽  
Zenith Tropospheric Delay ◽  
Background Data ◽  
Mean Square Errors

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations.

scholarly journals Applications of High-Resolution Imaging for Open Field Container Nursery Counting

2018 ◽  
Vol 10 (12) ◽  
pp. 2018 ◽  
Author(s):  
Ying She ◽  
Reza Ehsani ◽  
James Robbins ◽  
Josué Nahún Leiva ◽  
Jim Owen
Keyword(s):  
Vegetation Index ◽  
Ground Cover ◽  
Aerial Images ◽  
Support Vector ◽  
Inventory Data ◽  
Cover Type ◽  
Canopy Area ◽  
Resolution Imaging ◽  
Container Nurseries ◽  
The Stability

Frequent inventory data of container nurseries is needed by growers to ensure proper management and marketing strategies. In this paper, inventory data are estimated from aerial images. Since there are thousands of nursery species, it is difficult to find a generic classification algorithm for all cases. In this paper, the development of classification methods was confined to three representative categories: green foliage, yellow foliage, and flowering plants. Vegetation index thresholding and the support vector machine (SVM) were used for classification. Classification accuracies greater than 97% were obtained for each case. Based on the classification results, an algorithm based on canopy area mapping was built for counting. The effects of flight altitude, container spacing, and ground cover type were evaluated. Results showed that container spacing and interaction of container spacing with ground cover type have a significant effect on counting accuracy. To mimic the practical shipping and moving process, incomplete blocks with different voids were created. Results showed that the more plants removed from the block, the higher the accuracy. The developed algorithm was tested on irregular- or regular-shaped plants and plants with and without flowers to test the stability of the algorithm, and accuracies greater than 94% were obtained.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

scholarly journals Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Md. Matiur Rahaman ◽  
Md. Asif Ahsan ◽  
Ming Chen
Keyword(s):  
Data Mining ◽  
Agricultural Development ◽  
Computational Time ◽  
Support Vector ◽  
Phenotypic Traits ◽  
Linear Discriminant ◽  
Plant Phenomics ◽  
Classification Prediction ◽  
Linear Svm ◽  
Integrated Or

AbstractStatistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.

scholarly journals Hybrid Rule-Based Solution for Phishing URL Detection Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Youness Mourtaji ◽  
Mohammed Bouhorma ◽  
Daniyal Alghazzawi ◽  
Ghadah Aldabbagh ◽  
Abdullah Alghamdi
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Personal Information ◽  
Support Vector ◽  
Learning Models ◽  
Rule Based ◽  
Common Purpose ◽  
Behavioral Method ◽  
And Control ◽  
Similarity Method

The phenomenon of phishing has now been a common threat, since many individuals and webpages have been observed to be attacked by phishers. The common purpose of phishing activities is to obtain user’s personal information for illegitimate usage. Considering the growing intensity of the issue, this study is aimed at developing a new hybrid rule-based solution by incorporating six different algorithm models that may efficiently detect and control the phishing issue. The study incorporates 37 features extracted from six different methods including the black listed method, lexical and host method, content method, identity method, identity similarity method, visual similarity method, and behavioral method. Furthermore, comparative analysis was undertaken between different machine learning and deep learning models which includes CART (decision trees), SVM (support vector machines), or KNN ( K -nearest neighbors) and deep learning models such as MLP (multilayer perceptron) and CNN (convolutional neural networks). Findings of the study indicated that the method was effective in analysing the URL stress through different viewpoints, leading towards the validity of the model. However, the highest accuracy level was obtained for deep learning with the given values of 97.945 for the CNN model and 93.216 for the MLP model, respectively. The study therefore concludes that the new hybrid solution must be implemented at a practical level to reduce phishing activities, due to its high efficiency and accuracy.

scholarly journals Fault diagnosis of air-conditioning refrigeration system based on sparse autoencoder

2019 ◽  
Vol 14 (4) ◽  
pp. 487-492
Author(s):  
Zhiyi Wang ◽  
Jiachen Zhong ◽  
Jingfan Li ◽  
Cui Xia
Keyword(s):  
Fault Diagnosis ◽  
Air Conditioning ◽  
Large Scale ◽  
High Efficiency ◽  
Support Vector ◽  
Small Scale ◽  
Refrigeration System ◽  
Large Scale Data ◽  
Fault Features ◽  
Sparse Autoencoder

Abstract To overcome the drawbacks of using supervised learning to extract fault features for classification and low nonlinearity of the features in most of current fault diagnosis of air-conditioning refrigeration system, sparse autoencoder (SAE) is presented to extract fault features that are used as the input to the classifier and to achieve fault diagnosis for air-conditioning refrigeration system. The SAE structure is tuned by adjusting the number of hidden layers and nodes to build the optimal model, which is compared with the fault diagnosis model based on support vector machine. Results indicate that the indexes of the model combined with SAE, such as accuracy, precision and recall, are all improved, especially for the faults with high complexity. Besides, SAE shows high generalization ability with small-scale sample data and high efficiency with large-scale data. Obviously, the use of SAE can effectively optimize the diagnosis performance of the classifier.

Identification of a high efficiency boiler by support vector machines without bias term

2003 ◽  
Vol 36 (16) ◽  
pp. 465-470 ◽  
Author(s):  
Michael Vogt ◽  
Karsten Spreitzer ◽  
Vojislav Kecman
Keyword(s):  
Support Vector Machines ◽  
High Efficiency ◽  
Support Vector ◽  
Vector Machines

Research of MPPT Using Support Vector Machine for PV System

2013 ◽  
Vol 441 ◽  
pp. 268-271
Author(s):  
De Da Sun ◽  
Da Hai Zhang ◽  
Yang Liu
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
High Efficiency ◽  
Environmental Changes ◽  
Maximum Power ◽  
Solar Irradiation ◽  
Support Vector ◽  
Pv System ◽  
Maximum Power Point ◽  
Power Point

Photovoltaic (PV) power systems are widely used today, so its useful to study how to make the PV maximum power output. In this paper a novel approach based on Support Vector Machine (SVM) for maximum power point tracking (MPPT) of PV systems is presented. The output power characteristics of PV cells vary with solar irradiation and temperature, so the controllers inputs is the level of solar radiation and ambient temperature of the PV module, and the voltage at maximum power point (MPP) is the output. Results show that the proposed MPPT controller based on SVM is sensitive to environmental changes and has high efficiency and less Mean Square Error (MSE).

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