Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Yan, Yijun; Ren, Jinchang; Tschannerl, Julius; Zhao, Huimin; Harrison, Barry; Jack, Frances

doi:10.1109/tim.2021.3082274

Cited by 23 publications

(18 citation statements)

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“…In this work, four machine learning (ML) models i.e. Lasso regression, Ridge regression [7], Support Vector Machine (SVM) [8,9] and Random Forest (RF) [10,11] are used to evaluate the prediction performance of chlorophyll concentration. In the process of model training, the data set is randomly divided into a training set and testing set at a ratio of 8:2, resulting 13287 training samples and 3322 testing samples.…”

Section: Discussionmentioning

confidence: 99%

“…For the future work, we will work to collect more high-quality spectral data and insitu data. Once an expanded training data set is available, we will test novel band selection methods [13] and feature extraction methods [9] to extract the most useful information and help to get more accurate prediction results. Some chlorophyll algorithms such as [14] and [15] will be also useful for the data from specific ocean color sensors such as sentinel-3 and NASA, etc.…”

Section: )mentioning

confidence: 99%

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Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Yan

Zhang

Ren

et al. 2022

Lecture Notes in Electrical Engineering

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The Scottish Government is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in seawater quality monitoring as an indicator of eutrophication. Currently, Chl observations are mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper we explore implementation of advanced machine learning techniques to automatically estimate Chl via the historic time series of ocean colour remote sensing data from July 2002 to September 2019.

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Section: Discussionmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Yan

Zhang

Ren

et al. 2022

Lecture Notes in Electrical Engineering

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“…It reduces the incompleteness caused by man-made design features and has more advantages compared with OI, 4DVAR, and KF, conceptually. At present, machine learning has a very wide range of applications [16][17][18], such as language recognition, image recognition, data mining, and expert systems, etc. Some machine learning algorithms are used in the assimilation of ocean remote sensing data, such as genetic algorithms [19] and neural networks [20].…”

Section: Introductionmentioning

confidence: 99%

Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea

Wang¹,

Wang²,

Zhang³

et al. 2022

Applied Sciences

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The accurate temperature background field plays a vital role in the numerical prediction of sea surface temperature (SST). At present, the SST background field is mainly derived from multi-source data fusion, including satellite SST data and in situ data from marine stations, buoys, and voluntary observing ships. The characteristics of satellite SST data are wide coverage but low accuracy, whereas the in situ data have high accuracy but sparse distribution. For obtaining a more accurate temperature background field and realizing the fusion of measured data with satellite data as much as possible, we propose a sparse data-extended fusion method to predict SST in this paper. By using this method, the actual observed sites and buoys data in the East China Sea area are fused with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5.0 SST data. Furthermore, the temperature field in the study area were predicted by using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) deep learning methods, respectively. Finally, we obtained the results by traditional prediction methods to verify them. The experimental results show that the method we proposed in this paper can obtain more accurate prediction results, and effectively compensate for the uncertainty caused by the parameterization of ocean dynamic process, the discrete method, and the error of initial conditions.

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“…The greater availability of the technology and continuous increases in scene resolution [1], as well as the commoditization of data processing power and data storage, have made these images more common as a source of information to extract knowledge from. Hyperspectral and multispectral image processing [2] have been successfully used to perform a variety of semi-automated tasks as a way to increase the efficiency of productive processes in diverse fields [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A New Multispectral Data Augmentation Technique Based on Data Imputation

2021

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Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out.

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Nondestructive Phenolic Compounds Measurement and Origin Discrimination of Peated Barley Malt Using Near-Infrared Hyperspectral Imagery and Machine Learning

Abstract: Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning. IEEE transactions on instrumentation and measurement [online], Early Access.

Cited by 23 publications

References 54 publications

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water

Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea

A New Multispectral Data Augmentation Technique Based on Data Imputation

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