Using Deep Convolutional Neural Network for oak acorn viability recognition based on color images of their sections

[1]  Khan Muhammad,et al.  Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation , 2019, Multimedia Tools and Applications.

[2]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[3]  Tadeusz Juliszewski,et al.  Vision-based assessment of viability of acorns using sections of their cotyledons during automated scarification procedure , 2018, Bio Algorithms Med Syst..

[4]  Z. Kaliniewicz,et al.  Influence of Scarification on the Germination Capacity of Acorns Harvested from Uneven-aged Stands of Pedunculate Oak (Quercus robur L.) , 2018 .

[5]  R. Tadeusiewicz,et al.  Assessment of Selected Parameters of the Automatic Scarification Device as an Example of a Device for Sustainable Forest Management , 2017 .

[6]  R. Tadeusiewicz,et al.  Automation of the Acorn Scarification Process as a Contribution to Sustainable Forest Management. Case Study: Common Oak , 2017 .

[7]  Yang Xu,et al.  Weed identification based on K-means feature learning combined with convolutional neural network , 2017, Comput. Electron. Agric..

[8]  J. Chmielewski,et al.  Evaluation of the degree of healthiness of the pedunculate oak (Quercus robur L.) acorns in the Włoszczowa– Jędrzejów Nature Park and its neighbouring area , 2017 .

[9]  Wojciech Czarnecki,et al.  On Loss Functions for Deep Neural Networks in Classification , 2017, ArXiv.

[10]  Tomasz Kryjak,et al.  A compact deep convolutional neural network architecture for video based age and gender estimation , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[11]  Pablo M. Granitto,et al.  Deep learning for plant identification using vein morphological patterns , 2016, Comput. Electron. Agric..

[12]  Tobi Delbrück,et al.  Training Deep Spiking Neural Networks Using Backpropagation , 2016, Front. Neurosci..

[13]  Marcin Kurdziel,et al.  Encouraging orthogonality between weight vectors in pretrained deep neural networks , 2016, Neurocomputing.

[14]  Ryszard Tadeusiewicz,et al.  Colour-Based Binary Discrimination of Scarified Quercus robur Acorns under Varying Illumination , 2016, Sensors.

[15]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[16]  Xuelong Li,et al.  Speed up deep neural network based pedestrian detection by sharing features across multi-scale models , 2016, Neurocomputing.

[17]  Bernabé Linares-Barranco,et al.  ConvNets experiments on SpiNNaker , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[18]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[20]  Federico Pallottino,et al.  Colorimetric patterns of wood pellets and their relations with quality and energy parameters , 2014 .

[21]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[23]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[24]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[25]  Zhiguo Cao,et al.  Crop segmentation from images by morphology modeling in the CIE L*a*b* color space , 2013 .

[26]  Byun-Woo Lee,et al.  Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis , 2013 .

[27]  Bernabé Linares-Barranco,et al.  An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors , 2012, IEEE Journal of Solid-State Circuits.

[28]  M. Giertych,et al.  Consequences of cutting off distal ends of cotyledons of Quercus robur acorns before sowing , 2011, Annals of Forest Science.

[29]  X. Yi,et al.  Acorn germination and seedling survival of Q. variabilis: effects of cotyledon excision , 2010, Annals of Forest Science.

[30]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Maurizio Mencuccini,et al.  A noninvasive optical system for the measurement of xylem and phloem sap flow in woody plants of small stem size. , 2007, Tree physiology.

[32]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

[33]  Darren J. Kerbyson,et al.  Size invariant circle detection , 1999, Image Vis. Comput..

[34]  J. Kittler,et al.  Comparative study of Hough Transform methods for circle finding , 1990, Image Vis. Comput..

[35]  Yudong Zhang,et al.  Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling , 2017, Journal of Medical Systems.

[36]  P. Markowski,et al.  An analysis of the physical properties of seeds of selected deciduous tree species , 2016 .

[37]  Ryszard Tadeusiewicz,et al.  Neural networks as a tool for modeling of biological systems , 2015, Bio Algorithms Med Syst..

[38]  Leisa Armstrong,et al.  A survey of image processing techniques for agriculture , 2014 .

[39]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[40]  P. Markowski,et al.  Correlations between the germination capacity and selected physical properties of Scots pine (Pinus sylvestris L.) seeds , 2013 .

[41]  Xavier P. Burgos-Artizzu,et al.  utomatic segmentation of relevant textures in agricultural images , 2010 .

[42]  J. Weres,et al.  Neuronowa klasyfikacja obrazów suszu warzywnego , 2009 .

[43]  M. Górski,et al.  Zastosowanie sztucznych sieci neuronowych do oceny stopnia dojrzałości jabłek , 2008 .

[44]  P. Boniecki,et al.  Wpływ liczby zmiennych na jakość działania neuronowego modelu do identyfikacji mechanicznych uszkodzeń ziarniaków kukurydzy , 2008 .

[45]  Rastislav Lukac,et al.  Switching median filter with a local entropy control , 2003 .

[46]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[47]  Sven J. Dickinson,et al.  Object Representation and Recognition , 1999 .