scholarly journals Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques

2018 ◽  
Vol 10 (12) ◽  
pp. 2047 ◽  
Author(s):  
Jingjing Cao ◽  
Kai Liu ◽  
Yuanhui Zhu ◽  
Jun Li ◽  
Zhi He
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Reflectance Spectra ◽  
Machine Learning Techniques ◽  
Order Derivative ◽  
Mangrove Species ◽  
First Order ◽  
Learning Techniques ◽  
Close Range

Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi'ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa=0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa=0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa=0.9253) and 96.46% (Kappa=0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.

A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets

2021 ◽  
pp. 1-12
Author(s):  
Naman S. Bajaj ◽  
Abhishek D. Patange ◽  
R. Jegadeeshwaran ◽  
Kaushal A. Kulkarni ◽  
Rohan S. Ghatpande ◽  
...  
Keyword(s):  
Machine Learning ◽  
Discriminant Analysis ◽  
Condition Monitoring ◽  
Machining Process ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Analysis Model ◽  
Tool Condition ◽  
Learning Techniques ◽  
Optimization Search

Abstract With the advent of Industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing, etc. The significant research area in predictive maintenance is Tool Condition Monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool's condition in operation. These techniques are cost-saving and help industries with adopting future-proof solutions for their operations. One such technique called Discriminant analysis (DA) must be examined particularly for TCM. Owing to its less expensive computation and shorter run times, using them in TCM will ensure effective use of the cutting tool and reduce maintenance times. This paper presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data is collected using an in-house designed and developed Data Acquisition (DAQ) module set up on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter which gives the best model was found out to be ‘Linear’, achieving an accuracy of 93.3%. This work confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry-ready.

scholarly journals Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins

2021 ◽  
Vol 8 ◽  
Author(s):  
Lei Feng ◽  
Baohua Wu ◽  
Susu Zhu ◽  
Yong He ◽  
Chu Zhang
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Hyperspectral Imaging ◽  
Food Quality ◽  
Near Infrared ◽  
Imaging Techniques ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
Food Fraud ◽  
Learning Techniques

Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand.

Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques

2021 ◽  
Vol 171 ◽  
pp. 111356
Author(s):  
Sandra Munera ◽  
Juan Gómez-Sanchís ◽  
Nuria Aleixos ◽  
Joan Vila-Francés ◽  
Giancarlo Colelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Machine Learning Techniques ◽  
Learning Techniques

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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