Hyperspectral imaging combined with convolutional neural network for outdoor detection of potato diseases

2021 ◽  
Author(s):  
Feng Zhang ◽  
XinTing Li ◽  
Shuang Qiu ◽  
Jie Feng ◽  
DaWen Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
Potato Diseases

scholarly journals Convolutional neural network target detection in hyperspectral imaging for maritime surveillance

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984299
Author(s):  
Sara Freitas ◽  
Hugo Silva ◽  
José Miguel Almeida ◽  
Eduardo Silva
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Hyperspectral Imaging ◽  
Imaging System ◽  
Spectral Angle Mapper ◽  
Maritime Surveillance ◽  
Aerial Vehicles ◽  
Spectral Angle

This work addresses a hyperspectral imaging system for maritime surveillance using unmanned aerial vehicles. The objective was to detect the presence of vessels using purely spatial and spectral hyperspectral information. To accomplish this objective, we implemented a novel 3-D convolutional neural network approach and compared against two implementations of other state-of-the-art methods: spectral angle mapper and hyperspectral derivative anomaly detection. The hyperspectral imaging system was developed during the SUNNY project, and the methods were tested using data collected during the project final demonstration, in São Jacinto Air Force Base, Aveiro (Portugal). The obtained results show that a 3-D CNN is able to improve the recall value, depending on the class, by an interval between 27% minimum, to a maximum of over 40%, when compared to spectral angle mapper and hyperspectral derivative anomaly detection approaches. Proving that 3-D CNN deep learning techniques that combine spectral and spatial information can be used to improve the detection of targets classification accuracy in hyperspectral imaging unmanned aerial vehicles maritime surveillance applications.

scholarly journals Variety Identification of Single Rice Seed Using Hyperspectral Imaging Combined with Convolutional Neural Network

2018 ◽  
Vol 8 (2) ◽  
pp. 212 ◽  
Author(s):  
Zhengjun Qiu ◽  
Jian Chen ◽  
Yiying Zhao ◽  
Susu Zhu ◽  
Yong He ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
Variety Identification ◽  
Rice Seed ◽  
Single Rice

scholarly journals Intelligent Identification of MoS2 Nanostructures with Hyperspectral Imaging by 3D-CNN

Nanomaterials ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 1161 ◽  
Author(s):  
Kai-Chun Li ◽  
Ming-Yen Lu ◽  
Hong Thai Nguyen ◽  
Shih-Wei Feng ◽  
Sofya B. Artemkina ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
2D Materials ◽  
Image Resolution ◽  
Superior Performance ◽  
Color Contrast ◽  
Synthesis Methods ◽  
Specific Substrate ◽  
3D Cnn

Increasing attention has been paid to two-dimensional (2D) materials because of their superior performance and wafer-level synthesis methods. However, the large-area characterization, precision, intelligent automation, and high-efficiency detection of nanostructures for 2D materials have not yet reached an industrial level. Therefore, we use big data analysis and deep learning methods to develop a set of visible-light hyperspectral imaging technologies successfully for the automatic identification of few-layers MoS2. For the classification algorithm, we propose deep neural network, one-dimensional (1D) convolutional neural network, and three-dimensional (3D) convolutional neural network (3D-CNN) models to explore the correlation between the accuracy of model recognition and the optical characteristics of few-layers MoS2. The experimental results show that the 3D-CNN has better generalization capability than other classification models, and this model is applicable to the feature input of the spatial and spectral domains. Such a difference consists in previous versions of the present study without specific substrate, and images of different dynamic ranges on a section of the sample may be administered via the automatic shutter aperture. Therefore, adjusting the imaging quality under the same color contrast conditions is unnecessary, and the process of the conventional image is not used to achieve the maximum field of view recognition range of ~1.92 mm2. The image resolution can reach ~100 nm and the detection time is 3 min per one image.

Deep Feature Representation with Stacked Sparse Auto-Encoder and Convolutional Neural Network for Hyperspectral Imaging-Based Detection of Cucumber Defects

2018 ◽  
Vol 61 (2) ◽  
pp. 425-436 ◽  
Author(s):  
Ziyi Liu ◽  
Yong He ◽  
Haiyan Cen ◽  
Renfu Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
Defect Detection ◽  
Hyperspectral Image ◽  
Imaging System ◽  
Feature Representation ◽  
Inspection System ◽  
Deep Feature ◽  
Pickling Cucumbers

Abstract. It is challenging to achieve rapid and accurate processing of large amounts of hyperspectral image data. This research was aimed to develop a novel classification method by employing deep feature representation with the stacked sparse auto-encoder (SSAE) and the SSAE combined with convolutional neural network (CNN-SSAE) learning for hyperspectral imaging-based defect detection of pickling cucumbers. Hyperspectral images of normal and defective pickling cucumbers were acquired using a hyperspectral imaging system running at two conveyor speeds of 85 and 165 mm s-1. An SSAE model was developed to learn the feature representation from the preprocessed data and to perform five-class (normal, watery, split/hollow, shrivel, and surface defect) classification. To deal with a more complicated task for different types of surface defects (i.e., dirt/sand and gouge/rot classes) in six-class classification, a CNN-SSAE system was developed. The results showed that the CNN-SSAE system improved the classification performance, compared with the SSAE, with overall accuracies of 91.1% and 88.3% for six-class classification at the two conveyor speeds. Additionally, the average running time of the CNN-SSAE system for each sample was less than 14 ms, showing considerable potential for application in an automated on-line inspection system for cucumber sorting and grading. Keywords: Convolutional neural network, Defect detection, Hyperspectral imaging, Pickling cucumber, Representation learning, Stacked sparse auto-encoder.

scholarly journals Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network

2020 ◽  
Vol 50 (3) ◽  
Author(s):  
Wang Xiaoyan ◽  
Li Zhiwei ◽  
Wang Wenjun ◽  
Wang Jiawei
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Least Squares ◽  
Chlorophyll Content ◽  
Hyperspectral Imaging ◽  
Vegetation Index ◽  
Characteristic Parameter ◽  
Hyperspectral Data ◽  
Growth And Yield ◽  
Support Vector

ABSTRACT: Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic wavelengths, and the characteristic parameters were extracted based on the spectral and image information. A partial least squares regression (PLSR) prediction model was established based on the single characteristic parameter and multi-characteristic parameter fusion. The determination coefficient (Rv 2) and the root-mean-square error (RMSEv) of the validation set for the multi-characteristic parameter fusion model were reported to be 0.813 and 1.766, respectively, which are higher than those obtained by the single characteristic parameter model. Based on the multi-characteristic parameter fusion, an attention-convolutional neural network (attention-CNN) (Rv 2 = 0.839, RMSEv = 1.451, RPD = 2.355) was established, which is more effective than the PLSR (Rv 2 = 0.813, RMSEv = 1.766, RPD = 2.167) and least squares support vector machine (LS-SVM) models (Rv 2 = 0.806, RMSEv = 1.576, RPD = 2.061). These results indicated that the combination of hyperspectral imaging and attention-CNN is beneficial to the application of nutrient element monitoring of crops.

Gastric Carcinoma Classification Based on Convolutional Neural Network and Micro-Hyperspectral Imaging

2018 ◽  
Vol 38 (6) ◽  
pp. 0617001 ◽  
Author(s):  
杜剑 Du Jian ◽  
胡炳樑 Hu Bingliang ◽  
张周锋 Zhang Zhoufeng
Keyword(s):  
Neural Network ◽  
Gastric Carcinoma ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging

scholarly journals Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2831 ◽  
Author(s):  
Na Wu ◽  
Chu Zhang ◽  
Xiulin Bai ◽  
Xiaoyue Du ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Imaging ◽  
Evaluation System ◽  
Principal Component ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Training Set ◽  
Derivative Method ◽  
Discriminant Models

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874–1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.

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