Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties

Yu, Yang; Yao, Meibao

doi:10.1016/j.biosystemseng.2023.01.002

Cited by 10 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, to obtain a high-edge accuracy in an extremely challenging task, pure complex structures are insufficient, and an understanding of the data based on spectral expertise is essential. The wavelength interval selection method considers each wavelength interval as a unit, which improves the predictive capability, provides easier interpretation, and enables reliable calibration, such as: Moving windows PLS (MWPLS) [36], interval PLS (iPLS) [13], and synergy iPLS (SiPLS) [6]. As illustrated in Figure 2, when the stride step is smaller than the CKW, the convolution kernel overlaps with the spectral signals during movement, thus, more features can be extracted.…”

Section: Cnn Modeling and Training Processmentioning

confidence: 99%

“…Design of lightweight models: Hardware deployment for lightweight models is also an important future research direction for Mars rovers. The CNN spectral analysis method is combined with portable hardware [36,59,60] to promote the practical application of portable spectrometers in various fields. Two-dimensional CNNs have unique advantages in image feature extraction, but 1D CNNs are better matches in terms of dimensionality.…”

mentioning

confidence: 99%

See 1 more Smart Citation

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Modeling the quantitative relationship between target components and measured spectral information is an essential part of laser-induced breakdown spectroscopy (LIBS) analysis. However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.

show abstract

Section: Cnn Modeling and Training Processmentioning

confidence: 99%

mentioning

confidence: 99%

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

Yao

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, the use of Self-Compacting Concrete (SCC) has increased, and many articles have been published [14]. The introduction of SCC represents a significant technological advance leading to higher-quality concrete and efficient construction processes [15].…”

Section: Self-compacting Concretementioning

confidence: 99%

A Review of Self-Compacting Concrete Incorporating Waste Materials

Okokpujie,

Kelechi,

Tartibu

2023

ACSM

View full text Add to dashboard Cite

As environmental concerns gain prominence worldwide, the significance of sustainable building practices cannot be understated. The burgeoning production of waste annually exacerbates these concerns, with projections indicating a likely increase in volume and corresponding environmental pollution. This review study offers a sustainable approach to address this issue by exploring the potential of waste materials, namely, calcium carbide waste (CCW), crumb rubber (CR), and fly ash (FA), in the redevelopment of Self-Compacting Concrete (SCC). A comprehensive literature review was undertaken, encompassing several key areas: the properties of SCC, the influence of CCW on concrete workability, setting and strength (compressive, tensile, and flexural), and the characteristics of SCC incorporating crumb rubber. A detailed examination of design methodologies for SCC was conducted, including the Japanese design method, the European guidelines for SCC mixed design approach, and the mixed design according to BS EN 206:2013. Acceptance criteria for SCC set by various institutions were also evaluated, along with the composition and classification of rubber aggregates. Findings from the review suggest that CCW is a viable partial substitute for cement and cementitious materials in SCC. The consistency, along with the initial and final setting times of cement, exhibited an increase with the inclusion of FA and CCW. It is recommended that future studies consider up to a 50% FA content to produce grade 40 High Volume Fly Ash Self-Compacting Concrete (HVFA-SCC) with CCW and crumb rubber. This approach not only advances sustainable building practices but also proposes a novel solution for waste management.

show abstract

“…Several studies have employed NIRS and CVS techniques to diagnose diseases in many agricultural products [ 10 ]. For example, YanYu et al [ 11 ] established a tool for monitoring fruit quality from a combination of NIRS and chemical models and then utilized this tool to explore the construction of a generic model that, among other things, predicts the Soluble Solids Content (SSC) of thin-skinned fruits with similar physicochemical properties. Lei-Ming Yuan et al [ 12 ] employed vis-NIRS technology paired with a bias fusion modeling strategy for the noninvasive assessment of “Yunhe” pears, which improved the loss of spectral information in the optimized PLS model.…”

Section: Introductionmentioning

confidence: 99%

Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image

Chen

Liu

Rao

et al. 2023

Foods

View full text Add to dashboard Cite

The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.

show abstract

Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties

Cited by 10 publications

References 40 publications

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks

A Review of Self-Compacting Concrete Incorporating Waste Materials

Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image

Contact Info

Product

Resources

About