scholarly journals Real-Time Detection of Rice Growth Phase Transition for Panicle Nitrogen Application Timing Assessment

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2465
Author(s):  
Joon-Keat Lai ◽  
Wen-Shin Lin
Keyword(s):  
Phase Transition ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Logistic Regression ◽  
Growth Phase ◽  
Leaf Growth ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Growth Phase Transition ◽  
Leaf N

Nitrogen (N) topdressing at the early reproductive phase (ER) is beneficial for rice yield. However, the ER overlaps with the late vegetative phase (LV) and is, thus, difficult to be recognized by human observation. Therefore, this study aimed to establish a high-temporal-resolution approach to determine the LV and ER via hyperspectral proximal sensing. Firstly, this research measured the leaf cover area (LCA), leaf dry weight (LDW), chlorophyll content (SPAD), leaf N content (LNC), and leaf N accumulation (LNA) to investigate the physical and physiological changes of the rice plant during growth phase transition. It could be summarized that the LCA would be maximally extended before ER, the leaf growth would be retarded after LV, and leaves turned from green to yellowish-green resulting from N translocation. These phenomena were expected to be detected by the hyperspectral sensor. In order to capture the variation of spectral information while eliminating redundant hyperspectral wavelengths, feature extraction (FE) and feature selection (FS) were conducted to reduce the data dimension. Meanwhile, the implications of the features were also inferenced. Three principal components, which correlated with the rice plant’s physical and physiological traits, were extracted for subsequent modeling. On the aspect of FS, 402, 432, 579, and 696 nm were selected as the predictors. The 402 nm wavelength significantly correlated with leaf cover area to some extent (p < 0.09), and 432 nm had no significant correlation with all of the measured plant traits (p > 0.10). The 579 nm and 696 nm wavelengths were negatively correlated with SPAD and LNC (p < 0.001). In addition, 696 nm was also negatively correlated with LNA (p < 0.05). Finally, the logistic regression, random forest (RF), and support vector machine (SVM) algorithms were adopted to solve the binary classification problem. The result showed that the feature extraction-based logistic regression (FE-logistic) and support vector machine (FE-SVM) were competent for growth phase discrimination (accuracy > 0.80). Nonetheless, taking the detrimental effects of applying N at LV into consideration, the feature extraction-based support vector machine (FE-SVM) was more appropriate for the timing assessment of panicle fertilizer application (sensitivity > 0.90; specificity > 0.80; precision > 0.80).

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

Growth, phase transition, and island motion of Au on Ge(111)

2021 ◽  
Vol 155 (5) ◽  
pp. 054701
Author(s):  
J. A. Giacomo ◽  
C. H. Mullet ◽  
S. Chiang
Keyword(s):  
Phase Transition ◽  
Growth Phase ◽  
Growth Phase Transition

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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