Identification of wheat kernels by fusion of RGB, SWIR, and VNIR samples.

BACKGROUND The sustainable management of agricultural resources requires the integration of cutting-edge science with the observation and identification of crops. This assists experts to make correct decisions. The aim of this study is to assess the robustness of a commonly used deep learning tool, VGG16, in improving the categorization of wheat kernels. Two fusion methodologies were considered simultaneously. We performed experiments on visible light (RGB), short wave infrared (SWIR), and visible-near infrared (VNIR) datasets, including 40 classes, with 200 samples in each class, giving 8000 samples in total. RESULTS After making simulations with 6400 training and 1600 testing samples, we achieved excellent performance scores, with 98.19% and 100% accuracy rates, respectively. CONCLUSION The wheat identification system developed here serves as an effective identification framework and supports the view that deep learning tools can adequately discriminate between different types of wheat kernels. The proposed automated system would be useful for improving economic growth and in reducing the labor force, leading to greater efficiency and higher productivity in the wheat industry. © 2019 Society of Chemical Industry.

[1]  Josef Kittler,et al.  Infrared and Visible Image Fusion using a Deep Learning Framework , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[2]  A. Aghagolzadeh,et al.  Real-time fusion of multi-focus images for visual sensor networks , 2010, 2010 6th Iranian Conference on Machine Vision and Image Processing.

[3]  Sahin Isik,et al.  Wheat grain classification by using dense SIFT features with SVM classifier , 2016, Comput. Electron. Agric..

[4]  Alan R. Gillespie,et al.  Color enhancement of highly correlated images. II. Channel ratio and “chromaticity” transformation techniques , 1987 .

[5]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[6]  Weijun Li,et al.  Non-destructive identification of maize haploid seeds using nonlinear analysis method based on their near-infrared spectra , 2018, Biosystems Engineering.

[7]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Florent Perronnin,et al.  Fisher Kernels on Visual Vocabularies for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Lianxing Gao,et al.  Discriminating and elimination of damaged soybean seeds based on image characteristics , 2015 .

[10]  Hamid Reza Pourreza,et al.  Identification of nine Iranian wheat seed varieties by textural analysis with image processing , 2012 .

[11]  Sen Jia,et al.  Convolutional neural networks for hyperspectral image classification , 2017, Neurocomputing.

[12]  Pierre Bonnet,et al.  Categorizing plant images at the variety level: Did you say fine-grained? , 2016, Pattern Recognit. Lett..

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[14]  Henrik Skov Midtiby,et al.  Plant species classification using deep convolutional neural network , 2016 .

[15]  Rafael Rieder,et al.  Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review , 2018, Comput. Electron. Agric..

[16]  Yan-Fu Kuo,et al.  Identifying rice grains using image analysis and sparse-representation-based classification , 2016, Comput. Electron. Agric..

[17]  Tony P. Pridmore,et al.  Deep machine learning provides state-of-the-art performance in image-based plant phenotyping , 2016, bioRxiv.

[18]  Yang Tao,et al.  Automatic inspection machine for maize kernels based on deep convolutional neural networks , 2019, Biosystems Engineering.

[19]  Cordelia Schmid,et al.  Aggregating local descriptors into a compact image representation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  M. Bilginer Gülmezoglu,et al.  The common vector approach and its relation to principal component analysis , 2001, IEEE Trans. Speech Audio Process..

[21]  E. Schetselaar.  Fusion by the IHS transform: Should we use cylindrical or spherical coordinates? , 1998 .

[22]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Milad Abdollahzadeh,et al.  Multi-focus image fusion for visual sensor networks , 2016, 2016 24th Iranian Conference on Electrical Engineering (ICEE).