A residual neural network based method for the classification of tobacco cultivation regions using near-infrared spectroscopy sensors

Jiang, Daiyu; Qi, Guanqiu; Hu, Gang; Mazur, Neal; Zhu, Zhiqin; Wang, Di

doi:10.1016/j.infrared.2020.103494

Cited by 26 publications

(14 citation statements)

References 29 publications

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“…In analyzing and determining the nicotine percentage of tobacco leaves, the proposed 1D-FCN model was compared with two conventional machine-learning approaches, PLS regression methods [37], [38] and SVM [5], and a regular deep CNN model. We implemented both PLS and SVM approaches according to the steps described in their original papers.…”

Section: E Comparative Analysis With Other Methodsmentioning

confidence: 99%

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

Jiang

et al. 2021

JAIT

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As one chemical composition, nicotine content has an important influence on the quality of tobacco leaves. Rapid and non-destructive quantitative analysis of nicotine is an important task in the tobacco industry. Near-infrared (NIR) spectroscopy as an effective chemical-composition analysis technique has been widely used. In this paper, we propose a one-dimensional Fully Convolutional Network (1D-FCN) model to quantitatively analyze the nicotine composition of tobacco leaves using NIRspectroscopy data in a cloud environment. This 1D-FCN model uses one-dimension convolution layers to directly extract the complex features from sequential spectroscopy data. It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches. This research provides a deep learning foundation for quantitative analysis of NIR spectra data in the tobacco industry.

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Section: E Comparative Analysis With Other Methodsmentioning

confidence: 99%

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

Jiang

et al. 2021

JAIT

Self Cite

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“…For example, the authors in [27] present a one-dimensional fully convolutional network (1D-FCN) to quantitatively analyze the nicotine composition of tobacco leaves using NIRspectroscopy data via the cloud. A similar work [28] using the residual network (ResNet) is proposed for classifying regions of tobacco cultivations.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

Ponce

Cevallos

Espinosa

et al. 2021

JAIT

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Tomato crops are considered the most important agricultural products worldwide. However, the quality of tomatoes depends mainly on the nutrient levels. Visual inspection is made by farmers to anticipate the nutrient deficiency of the plants. Recently, precision agriculture has explored opportunities to automate nutrient level monitoring. Previous work has demonstrated that a convolutional neural network (CNN) is able to estimate low nutrients in tomato plants using images of their leaves. However, the performance of the CNN was not adequate. Thus, this work proposes a novel CNNbased classifier, namely CNN+AHN, for estimating low nutrients in tomato crops using an image of the tomato leaves. The CNN+AHN incorporates a set of convolutional layers as the feature extraction part, and a supervised learning method called artificial hydrocarbon network (AHN) as the dense layer. Different combinations of the architecture of CNN+AHN were examined. Experimental results showed that our best CNN+AHN classifier is able to estimate low nutrients in tomato plants with an accuracy of 95:57% and F1-score of 95:75%, outperforming the literature.

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“…How the system integrates multiple parts is essential for organizing local features. Aggregating multiple part-level local features by multiple loss functions [ 23 , 24 ] can guide the network to learn a robust representation for unseen persons.…”

Section: Related Workmentioning

confidence: 99%

EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID

Wang

et al. 2021

J. Imaging

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Person re-identification (Re-ID) is challenging due to host of factors: the variety of human positions, difficulties in aligning bounding boxes, and complex backgrounds, among other factors. This paper proposes a new framework called EXAM (EXtreme And Moderate feature embeddings) for Re-ID tasks. This is done using discriminative feature learning, requiring attention-based guidance during training. Here “Extreme” refers to salient human features and “Moderate” refers to common human features. In this framework, these types of embeddings are calculated by global max-pooling and average-pooling operations respectively; and then, jointly supervised by multiple triplet and cross-entropy loss functions. The processes of deducing attention from learned embeddings and discriminative feature learning are incorporated, and benefit from each other in this end-to-end framework. From the comparative experiments and ablation studies, it is shown that the proposed EXAM is effective, and its learned feature representation reaches state-of-the-art performance.

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A residual neural network based method for the classification of tobacco cultivation regions using near-infrared spectroscopy sensors

Cited by 26 publications

References 29 publications

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

Estimation of Low Nutrients in Tomato Crops Through the Analysis of Leaf Images Using Machine Learning

EXAM: A Framework of Learning Extreme and Moderate Embeddings for Person Re-ID

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