Journal ArticleDOI
A review of deep learning used in the hyperspectral image analysis for agriculture
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TLDR
Its applications in agriculture are summarized, include ripeness and component prediction, different classification themes, and plant disease detection, and the prospects of future works are put forward.Abstract:
Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can capture up to several hundred images of different wavelengths and offer relevant spectral signatures. Hyperspectral imaging technology has achieved breakthroughs in the acquisition of agricultural information and the detection of external or internal quality attributes of the agricultural product. Deep learning techniques have boosted the performance of hyperspectral image analysis. Compared with traditional machine learning, deep learning architectures exploit both spatial and spectral information of hyperspectral image analysis. To scrutinize thoroughly the current efforts, provide insights, and identify potential research directions on deep learning for hyperspectral image analysis in agriculture, this paper presents a systematic and comprehensive review. Firstly, its applications in agriculture are summarized, include ripeness and component prediction, different classification themes, and plant disease detection. Then, the recent achievements are reviewed in hyperspectral image analysis from the aspects of the deep learning models and the feature networks. Finally, the existing challenges of hyperspectral image analysis based on deep learning are summarized and the prospects of future works are put forward.read more
Citations
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Journal ArticleDOI
Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish
Daoliang Li,Ling Du +1 more
TL;DR: In this paper, the authors present the challenges and benchmarks in terms of the advantages and disadvantages of the selected method in each field, and review the sources of image acquisition and pre-processing methods in aquaculture.
Journal ArticleDOI
Deep learning for near-infrared spectral data modelling: Hypes and benefits
Puneet Mishra,Dário Passos,Federico Marini,Junli Xu,José Manuel Amigo,Aoife Gowen,Jeroen J. Jansen,Alessandra Biancolillo,Jean-Michel Roger,Douglas N. Rutledge,Alison Nordon +10 more
TL;DR: In this paper , the authors provide a critical and comprehensive review of the major benefits, and potential pitfalls, of current DL tecniques used for spectral data modelling and provide empirical guidelines on the best practice for the use of DL for the modelling of spectral data.
Journal ArticleDOI
Developing deep learning based regression approaches for prediction of firmness and pH in Kyoho grape using Vis/NIR hyperspectral imaging
TL;DR: In this paper , stacked auto-encoders were applied to extract deep spectral features based on the pixel-level spectra of each sample over the wavelength range of 400.68-1001.61 nm.
Journal ArticleDOI
Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning
TL;DR: Wang et al. as discussed by the authors used a self-built deep learning model LPnet to assess the severity of rice bacterial blight (BB) lesion at the time scale and extracted the most informative spectral features related to lesion proportion.
Journal ArticleDOI
Recent Advances in Multi- and Hyperspectral Image Analysis
TL;DR: In this article, a range of image processing and machine learning analysis pipelines have been developed to extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.