A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves

Zhou, Xin; Sun, Jun; Tian, Yan; Chen, Quansheng; Wu, Xiaohong; Hang, Yingying

doi:10.1016/j.chemolab.2020.103996

Cited by 65 publications

(27 citation statements)

References 25 publications

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“…The authors in [53] proposed a deep learning-based regression approach to utilize hyperspectral data for the prediction of cadmium residue in lettuce leaves. Their deep learning approach consisted of stacked auto-encoders (SAE) and partial least squares support vector machine regression (LSSVR).…”

Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

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Section: Deep Learning Techniques For Hyperspectral Data Analyticsmentioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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“…Lettuce ( Lactuca sativa L. var. longifolia ) is one of the most popular vegetables in the world, rich in vitamins, carotenoids, dietary fiber, and other trace elements ( Kim et al, 2016 ; Xin et al, 2020 ). Moreover, the soluble solids content (SSC) and pH in biochemical traits are key indicators of lettuce taste and harvest time, and thus it is crucial in the lettuce growing industry ( Eshkabilov et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce

Fan

et al. 2022

Front. Plant Sci.

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The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed via multivariate analysis methods. In this study, two end-to-end deep learning models were developed based on 2D convolutional neural networks (2DCNN) and fully connected neural networks (FCNN; Deep2D and DeepFC, respectively) to rapidly and non-destructively predict the phenotyping traits of lettuces from spectral reflectance. Three linear and two nonlinear multivariate analysis methods were used to develop models to weigh the performance of the deep learning models. The models based on multivariate analysis methods require a series of manual feature extractions, such as pretreatment and wavelength selection, while the proposed models can automatically extract the features in relation to phenotyping traits. A visible near-infrared hyperspectral camera was used to image lettuce plants growing in the field, and the spectra extracted from the images were used to train the network. The proposed models achieved good performance with a determination coefficient of prediction (Rp2) of 0.9030 and 0.8490 using Deep2D for soluble solids content and DeepFC for pH, respectively. The performance of the deep learning models was compared with five multivariate analysis method. The quantitative analysis showed that the deep learning models had higher Rp2 than all the multivariate analysis methods, indicating better performance. Also, wavelength selection and different pretreatment methods had different effects on different multivariate analysis methods, and the selection of appropriate multivariate analysis methods and pretreatment methods increased more time and computational cost. Unlike multivariate analysis methods, the proposed deep learning models did not require any pretreatment or dimensionality reduction and thus are more suitable for application in high-throughput plant phenotyping platforms. These results indicate that the deep learning models can better predict phenotyping traits of plants using spectral reflectance.

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“…In recent years, due to fast progress in the domains of artificial intelligence (AI) and deep neural networks (DNNs), deep learning (DL) methodologies have been slowly diffusing into the realm of chemometrics supplying a large potential to model NIR spectral data [16,17]. For predictive modelling i.e., regression, DL has already outperformed traditional chemometrics and classic machine learning approaches [18][19][20]. Although new algorithms are appearing in the literature every day, currently, the state of the art for spectral data modelling can be subdivided into two approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Although new algorithms are appearing in the literature every day, currently, the state of the art for spectral data modelling can be subdivided into two approaches. The first is the use of deep autoencoders to extract complex features from the spectral data, which can then be combined with either a neural network or a traditional machine learning approach such as SVM for predictive modelling [18,19]. The second approach involves the use of Convolutional Neural Networks (CNNs) architectures for joint feature extraction and predictive modelling [17,21].…”

Section: Introductionmentioning

confidence: 99%

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling

Passos

Mishra

2021

Chemometrics and Intelligent Laboratory Systems

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In deep learning (DL) modelling for spectral data, a major challenge is related to the choice of DL network architecture and the selection of the best hyperparameters. Often, slight changes to the neural architecture or its hyperparameter can have a direct influence on the model's performance, making its robustness questionable. To deal with it, this study presents an automated deep learning modelling based on advanced optimisation techniques involving Hyperband and Bayesian optimisation, to automatically find optimal neural architecture and its hyperparameters to reach robust DL models. The optimisation requires a base neural architecture to be initialized, however, later it automatically adjusts the neural architecture and the hyperparameters to reach the optimal model. Furthermore, to support the interpretation of the DL models, a wavelength weighing schema based on gradient-weighted class activation mapping (Grad-CAM) was implemented. The potential of the approach was showed on a real case of wheat variety classification with near-infrared spectral data. The performance of the classification was compared with that previously reported on the same dataset with different DL and chemometric approaches. The results showed that with the proposed approach a classification accuracy of 94.9% was reached, which was better than the best reported accuracy on the same data set i.e., 93%. Furthermore, the better performance was obtained with a simpler neural architecture compared to what was used in earlier studies. The automated deep learning based on advanced optimisation can support DL modelling of spectral data.

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A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves

Cited by 65 publications

References 25 publications

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling

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