Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy

Ni, Chao; Zhang, Yun; Wang, Dongyi

doi:10.1155/2018/8696202

Cited by 7 publications

(5 citation statements)

References 26 publications

(26 reference statements)

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“…This value is close to the regression line, which indicates that the model has higher prediction accuracy. Ni et al [22] applied NIR spectroscopy and the stacked autoencoder combined with support vector regression to establish a prediction model for the moisture content of Masson pine seedling leaves. The R 2 C and R 2 P are 0.9946 and 0.9621, respectively, and the RMSEC and RMSEP are 0.1636 0.4249, respectively.…”

Section: Validation For Unknown Samplesmentioning

confidence: 99%

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Zhang

Rita-Cindy

2019

Molecules

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Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set ( R C 2 ), R-square of the cross-validation set ( R C V 2 ) and R-square of the prediction set ( R P 2 ) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The R C 2 , R C V 2 and R P 2 of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has R C 2 , R C V 2 , and R P 2 of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.

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Section: Validation For Unknown Samplesmentioning

confidence: 99%

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Zhang

Rita-Cindy

2019

Molecules

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“…Traditional autoencoders [32][33][34] are generally fully connected, which will generate a large number of redundant parameters. The extracted features are global, local features are ignored, and local features are more important for wood texture recognition.…”

Section: Methods Of the Local Feature Descriptor Based On The Convolumentioning

confidence: 99%

Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Liu

Ding

et al. 2020

Sensors

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Considering the difficult problem of robot recognition and grasping in the scenario of disorderly stacked wooden planks, a recognition and positioning method based on local image features and point pair geometric features is proposed here and we define a local patch point pair feature. First, we used self-developed scanning equipment to collect images of wood boards and a robot to drive a RGB-D camera to collect images of disorderly stacked wooden planks. The image patches cut from these images were input to a convolutional autoencoder to train and obtain a local texture feature descriptor that is robust to changes in perspective. Then, the small image patches around the point pairs of the plank model are extracted, and input into the trained encoder to obtain the feature vector of the image patch, combining the point pair geometric feature information to form a feature description code expressing the characteristics of the plank. After that, the robot drives the RGB-D camera to collect the local image patches of the point pairs in the area to be grasped in the scene of the stacked wooden planks, also obtaining the feature description code of the wooden planks to be grasped. Finally, through the process of point pair feature matching, pose voting and clustering, the pose of the plank to be grasped is determined. The robot grasping experiment here shows that both the recognition rate and grasping success rate of planks are high, reaching 95.3% and 93.8%, respectively. Compared with the traditional point pair feature method (PPF) and other methods, the method present here has obvious advantages and can be applied to stacked wood plank grasping environments.

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“…In this set of experiments, each method uses the same data to train the model, and determines the model parameters, and then model performance was evaluated using the same test data. The experimental results are shown in Table 4, in which the PLSR, SVR, and ANN models for moisture content measurement are taken from the literature [21], and the PLSR, SVR, and ANN models for nitrogen content measurement are taken from the literature [5]. To sum up, the predictive ability of the MS-CNN model, after improving the convolution kernel, is better than that of the CNN model, but these two models cannot be trained because of the deep network depth and the gradient extinction problem.…”

Section: Performance Evaluation Of Published Measurement Modelsmentioning

confidence: 99%

“…The experimental results are shown in Table 4, in which the PLSR, SVR, and ANN models for moisture content measurement are taken from the literature [21], and the PLSR, SVR, and ANN models for nitrogen content measurement are taken from the literature [5]. [21], and the PLSR, SVR, and ANN models for nitrogen content measurement are taken from the literature [5].…”

Section: Performance Evaluation Of Published Measurement Modelsmentioning

confidence: 99%

“…A multi-scale convolutional network divides the network into blocks and limits the number of output channels in each layer to reduce the number of parameters and the computational complexity, and to prevent over-fitting. Ni et al used stacked automatic encoders to extract layered output-related features layer by layer and then applied the SVR model to accurately predict the water content of Pinus massoniana leaves [21]. In another study, Wang et al used variableweighted CNN to improve the one-dimensional CNN model's generalization ability and predicted the nitrogen content of Pinus massoniana leaves.…”

Section: Introductionmentioning

confidence: 99%

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Non-Destructive Testing of Moisture and Nitrogen Content in Pinus Massoniana Seedling Leaves with NIRS Based on MS-SC-CNN

Huang

Zhu

et al. 2021

Applied Sciences

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Pinus massoniana is a pioneer reforestation tree species in China. It is crucial to evaluate the seedling vigor of Pinus massoniana for reforestation work, and leaf moisture and nitrogen content are key factors used to achieve it. In this paper, we proposed a non-destructive testing method based on the multi-scale short cut convolutional neural network (MS-SC-CNN) to measure moisture and nitrogen content in leaves of Pinus massoniana seedlings. By designing a reasonable short cut structure, the method realized the transmission of loss function gradient across the multi-layer structure in the network and reduced the information loss caused by the multi-layer transmission in the forward propagation. Meanwhile, in the back propagation stage, the loss caused by the multi-layer transmission of gradient was reduced. Thus, the gradient vanishing problem in training was avoided. Since the method realized cross-layer transmission error, the convolutional layer could be increased appropriately to obtain higher measurement accuracy. To verify the performance of the proposed MS-SC-CNN non-destructive measurement method, the near-infrared hyperspectral data of sample leaves of 219 Pinus massoniana seedlings were collected from the Huangping Forest Farm in Guizhou Province. The correlation coefficient between the measured and real values of the prediction was as high as 0.977 and the root mean square error was 0.242 for the moisture content of Pinus massoniana seedling leaves. For the nitrogen content of Pinus massoniana seedling leaves, the correlation coefficient between the measured and real values of the prediction was 0.906 and the root-mean-square error was 0.061. The results showed that the non-destructive testing method based on MS-SC-CNN that we proposed can accurately measure the moisture and nitrogen content in leaves of Pinus massoniana seedlings.

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Moisture Content Quantization of Masson Pine Seedling Leaf Based on Stacked Autoencoder with Near-Infrared Spectroscopy

Cited by 7 publications

References 26 publications

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis

Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Non-Destructive Testing of Moisture and Nitrogen Content in Pinus Massoniana Seedling Leaves with NIRS Based on MS-SC-CNN

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