Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder

Shen, Luxiang; Wang, Honghong; Liu, Ying; Liu, Yang; Zhang, Xiao; Fei, Yeqi

doi:10.3390/app10113769

Cited by 18 publications

(14 citation statements)

References 36 publications

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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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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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“…By using visible/short-wave near-infrared spectroscopy to establish a partial least squares (PLS) model that can evaluate tomato pH, the results showed that the PLS model offers good prediction ability, with a predicted correlation coefficient of 0.796. Shen [ 13 ] proposed a model combining sparse autoencoder (SAE) and partial least square regression (PLSR) (SAE-PLSR) to predict the soluble solid content of green plum. The correlation coefficient and the root mean square error of the prediction set were 0.9254 and 0.6827, respectively, which indicates excellent prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

Liu

Wang

Fei

et al. 2021

Sensors

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The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.

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“…Shen et al combined the PLSR method based on stack self-coding with hyperspectral imaging technology to realize the rapid non-destructive testing of the solid soluble content in green plums [16]. Although this more in-depth learning network has better nonlinearity, due to the increase of the network's depth, the gradient of the backpropagation loss function may disappear during training, so that the weight of the network cannot be effectively adjusted.…”

Section: Introductionmentioning

confidence: 99%

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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Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder

Cited by 18 publications

References 36 publications

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

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

Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

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

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