Hyperspectral Estimation of Apple Canopy Chlorophyll Content Using an Ensemble Learning Approach

2021 ◽  
Vol 37 (3) ◽  
pp. 505-511
Author(s):  
Xueyuan Bai ◽  
Yingqiang Song ◽  
Ruiyang Yu ◽  
Jingling Xiong ◽  
Yufeng Peng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Regression Model ◽  
Chlorophyll Content ◽  
Ensemble Learning ◽  
Vegetation Indices ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Learning Approach ◽  
Stress Monitoring ◽  
Apple Trees

HighlightsMonitored the canopy chlorophyll content of apple trees using hyperspectral reflectance information.Constructed support vector machine combination regression model (C-SVR) based on five-fold cross validation and support vector machine regression approach.Compared estimation accuracy of ensemble learning models (C-SVR, RF), machine learning models (SVR, ANN), and PLSR models for apple canopy chlorophyll content.Abstract. Rapidly and effective monitoring of the canopy chlorophyll content (CCC) of apple trees is of great significance for crop stress monitoring in precision agriculture. This study attempted to use hyperspectral vegetation indices (VIs) to estimate the CCC of apple trees based on ensemble learning approach. In this study, vegetation indices combined by any two wavelengths from 400 to 1100 nm were constructed to calculate the correlation coefficient with the CCC in apple. We constructed a partial least squares regression model (PLSR), artificial neural network regression model (ANN), support vector machine regression (SVR), random forest regression (RF) model and support vector machine combination regression model (C-SVR) based on combinations of VIs to improve the estimation accuracy in apple CCC. The results showed that the correlation coefficients between NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), DVI (572,532), and apple CCC were all above 0.76. The CCC estimation model using the RF and C-SVR approach constructed by the NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), and DVI (572,532) achieved the better estimation results, and the R2V, RMSEV, and RPDV values of models were 0.76, 0.131(mg . g-1), 2.04 and 0.78, 0.127(mg . g-1), 2.12, respectively. Compared with the PLSR, ANN, and SVR model, the R2V and RPDV values of C-SVR model were increased by 4%, 1.2%, 3.8%, and 5.0%, 28.4%, 7.1%, respectively. The results show that using C-SVR approach to estimating the apple CCC can realize high accuracy of quantitative estimation. Ensemble learning approach is an effective method for monitoring the nutrient status of fruit trees based on hyperspectral technique. Keywords: Apple tree canopy, Chlorophyll content, Crop stress monitoring, Ensemble learning, Hyperspectral, Vegetation index.

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

scholarly journals Support Vector Machine for Regional Ionospheric Delay Modeling

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2947 ◽  
Author(s):  
Zhengxie Zhang ◽  
Shuguo Pan ◽  
Chengfa Gao ◽  
Tao Zhao ◽  
Wang Gao
Keyword(s):  
Support Vector Machine ◽  
Regression Model ◽  
Dimensional Space ◽  
Back Propagation ◽  
Single Frequency ◽  
Support Vector ◽  
Electron Content ◽  
Total Electron ◽  
Ionosphere Modeling ◽  
Mean Square Errors

The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

scholarly journals Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6319
Author(s):  
Chia-Sheng Tu ◽  
Chih-Ming Hong ◽  
Hsi-Shan Huang ◽  
Chiung-Hsing Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Regression Model ◽  
Wind Power ◽  
Support Vector ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Power Forecasting ◽  
Data Regression ◽  
Power Forecasting

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.

Temperature Compensation of RLG’s Bias Based on LS-SVM

2011 ◽  
Vol 298 ◽  
pp. 1-6
Author(s):  
Guo Wei ◽  
Peng Fei Zhang ◽  
Xun Jin ◽  
Xing Wu Long
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Regression Model ◽  
Least Squares ◽  
Ring Laser ◽  
Temperature Compensation ◽  
Maximum Error ◽  
Support Vector ◽  
Least Squares Regression ◽  
Dynamic Temperature

. Bias of ring laser gyro (RLG) changes with temperature in the non-linear way, which is an important restraining factor for improving the accuracy of RLG. For the deficiency of least squares regression and neural networks, a new method of temperature compensation of RLG’s bias was proposed, that is, building function regression model by using Least Squares-Support Vector Machine(LS-SVM). Static and dynamic temperature experiments of RLG’s bias are carried out. The results show that: after static temperature compensation, the maximum error of RLG’s bias has dropped from 0.0413º/hr to 0.00073º/hr; while after dynamic temperature compensation, the gyro precision has increased from = 0.0102º/hr to = 0.0011º/hr. It indicates that this method has improved the laser gyro’s accuracy considerably.

scholarly journals Landslide Scars Detection using Remote Sensing and Pattern Recognition Techniques: Comparison Among Artificial Neural Networks, Gaussian Maximum Likelihood, Random Forest, and Support Vector Machine Classifiers

2020 ◽  
Vol 72 (4) ◽  
pp. 665-680
Author(s):  
Tatiana Dias Tardelli Uehara ◽  
Sabrina Paes Leme Passos Corrêa ◽  
Renata Pacheco Quevedo ◽  
Thales Sehn Körting ◽  
Luciano Vieira Dutra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Random Forest ◽  
Risk Mitigation ◽  
Vegetation Indices ◽  
Support Vector ◽  
Visual Interpretation ◽  
Artificial Neural

Landslide inventory is an essential tool to support disaster risk mitigation. The inventory is usually obtained via conventional methods, as visual interpretation of remote sensing images, or semi-automatic methods, through pattern recognition. In this study, four classification algorithms are compared to detect landslides scars: Artificial Neural Network (ANN), Maximum Likelihood (ML), Random Forest (RF) and Support Vector Machine (SVM). From Sentinel-2A imagery and SRTM’s Digital Elevation Model (DEM), vegetation indices and slope features were extracted and selected for two areas at the Rolante River Catchment, in Brazil. The classification products showed that the ML and the RF presented superior results with OA values above 92% for both study areas.  These best accuracy’s results were identified in classifications using all attributes as input, so without previous feature selection.

scholarly journals Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM)

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 18-26
Author(s):  
Hendry Cipta Husada ◽  
Adi Suryaputra Paramita
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Learning Approach ◽  
Social Media Platform ◽  
Machine Learning Approach ◽  
Media Platform ◽  
Fold Cross Validation

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

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