Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes From Visual Images of Wheat Plants

Computer vision with deep learning is emerging as a significant approach for non-invasive and non-destructive plant phenotyping. Spikes are the reproductive organs of wheat plants. Detection and counting of spikes considered the grain-bearing organ have great importance in the phenomics study of large sets of germplasms. In the present study, we developed an online platform, “Web-SpikeSegNet,” based on a deep-learning framework for spike detection and counting from the wheat plant’s visual images. The architecture of the Web-SpikeSegNet consists of 2 layers. First Layer, Client-Side Interface Layer, deals with end user’s requests and corresponding responses management. In contrast, the second layer, Server Side Application Layer, consists of a spike detection and counting module. The backbone of the spike detection module comprises of deep encoder-decoder network with hourglass network for spike segmentation. The Spike counting module implements the “Analyze Particle” function of imageJ to count the number of spikes. For evaluating the performance of Web-SpikeSegNet, we acquired the wheat plant’s visual images, and the satisfactory segmentation performances were obtained as Type I error 0.00159, Type II error 0.0586, Accuracy 99.65%, Precision 99.59% and F1 score 99.65%. As spike detection and counting in wheat phenotyping are closely related to the yield, Web-SpikeSegNet is a significant step forward in the field of wheat phenotyping and will be very useful to the researchers and students working in the domain.

[1]  Ajay Kumar,et al.  An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Pouria Sadeghi-Tehran,et al.  DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks , 2019, Front. Plant Sci..

[3]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[4]  Francesco Visin,et al.  A guide to convolution arithmetic for deep learning , 2016, ArXiv.

[5]  Eric A. Meyer,et al.  Cascading Style Sheets: The Definitive Guide , 2000 .

[6]  Francisco Javier Blanco Silva.  Learning SciPy for Numerical and Scientific Computing , 2013 .

[7]  Sandro Tosi,et al.  Matplotlib for Python Developers , 2009 .

[8]  Hanno Scharr,et al.  Machine Learning for Plant Phenotyping Needs Image Processing. , 2016, Trends in plant science.

[9]  S. Christensen,et al.  Deconstructing crop processes and models via identities. , 2013, Plant, cell & environment.

[10]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  John Riley,et al.  Tim Berners-Lee , 1998 .

[12]  Benjamin Graham,et al.  Fractional Max-Pooling , 2014, ArXiv.

[13]  J. Cai,et al.  Detecting spikes of wheat plants using neural networks with Laws texture energy , 2017, Plant Methods.

[14]  Tony P. Pridmore,et al.  Deep Learning for Multi-task Plant Phenotyping , 2017, bioRxiv.

[15]  Meng Yang,et al.  Large-Margin Softmax Loss for Convolutional Neural Networks , 2016, ICML.

[16]  Pouria Sadeghi-Tehran,et al.  Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering , 2017, Front. Plant Sci..

[17]  Bi Kun,et al.  Non-destructive measurement of wheat spike characteristics based on morphological image processing , 2010 .

[18]  Luís A. Alexandre,et al.  The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Michal Haindl,et al.  Unsupervised detection of non-iris occlusions , 2015, Pattern Recognit. Lett..

[20]  A Asundi,et al.  Fast phase-unwrapping algorithm based on a gray-scale mask and flood fill. , 1998, Applied optics.

[21]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[22]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[23]  Pierre Baldi,et al.  Learning Activation Functions to Improve Deep Neural Networks , 2014, ICLR.

[24]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[25]  Hamid Laga,et al.  Detection and analysis of wheat spikes using Convolutional Neural Networks , 2018, Plant Methods.

[26]  M. Welsh,et al.  Flask : A Language for Data-driven Sensor Network Programs , 2006 .

[27]  Francisco J. Blanco-Silva.  Learning SciPy for Numerical and Scientific Computing , 2013 .

[28]  Gaurav Jaswal,et al.  PixISegNet: pixel-level iris segmentation network using convolutional encoder-decoder with stacked hourglass bottleneck , 2020, IET Biom..

[29]  Aditya Nigam,et al.  SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging , 2020, Plant Methods.

[30]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[31]  F. Baret,et al.  Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods , 2020, Plant phenomics.

[32]  Hao Lu,et al.  TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks , 2019, Plant Methods.