Measuring the maturity of unopened cotton bolls with near infrared spectroscopy

Long, Robert L.; Bange, Michael

doi:10.1177/0967033520911463

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…In addition, numerous studies on the identification of tobacco varieties [9], tobacco parts [10], tobacco grades [11][12][13], aroma styles [14], and planting areas [15,16] using NIR spectroscopy techniques have also been carried out. More specifically, the distinguishing ability of NIR spectroscopy has been evaluated to determine the maturity levels of avocados [17][18][19][20], tomatoes [21,22], lychees [23], pomegranates [24], dates [25], table grapes [26], watermelons [27], cotton bolls [28], truffles [29], white teas [30], and peaches [31]. Despite the increasing number of applications of NIR spectroscopy in crop and fruit quality assessments, there are still only a few reports regarding the use of this technique to classify the maturity levels of fresh tobacco leaves.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Chen

Bin

Zou

et al. 2021

Journal of Analytical Methods in Chemistry

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The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

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Section: Introductionmentioning

confidence: 99%

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Chen

Bin

Zou

et al. 2021

Journal of Analytical Methods in Chemistry

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Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

et al. 2022

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During spinning, the chemical component content of natural fibers has a great influence on the mechanical properties. How to rapidly and accurately measure these properties has become the focus of the industry. In this work, a grey model (GM) for rapid and accurate prediction of the mechanical properties of windmill palm fiber (WPF) was established to explore the effect of chemical component content on the Young’s modulus. The chemical component content of cellulose, hemicellulose, and lignin in WPF was studied using near-infrared (NIR) spectroscopy, and an NIR prediction model was established, with the measured chemical values as the control. The value of RC and RCV were more than 0.9, while the values of RMSEC and RMSEP were less than 1, which reflected the excellent accuracy of the NIR model. External validation and a two-tailed t-test were used to evaluate the accuracy of the NIR model prediction results. The GM(1,4) model of WPF chemical components and the Young’s modulus was established. The model indicated that the increase in cellulose and lignin content could promote the increase in the Young’s modulus, while the increase in hemicellulose content inhibited it. The establishment of the two models provides a theoretical basis for evaluating whether WPF can be used in spinning, which is convenient for the selection of spinning fibers in practical application.

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Measuring the maturity of unopened cotton bolls with near infrared spectroscopy

Cited by 2 publications

References 12 publications

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

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