Fruit recognition from images using deep learning

Abstract In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use such classifier.

[1]  R. N. Shebiah,et al.  Fruit Recognition using Color and Texture Features , 2010 .

[2]  Maryam Rahnemoonfar,et al.  Deep Count: Fruit Counting Based on Deep Simulated Learning , 2017, Sensors.

[3]  Yael Edan,et al.  Computer vision for fruit harvesting robots - state of the art and challenges ahead , 2012, Int. J. Comput. Vis. Robotics.

[4]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[5]  Jürgen Schmidhuber,et al.  Training Very Deep Networks , 2015, NIPS.

[6]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[7]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[8]  James Patrick Underwood,et al.  Deep fruit detection in orchards , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[9]  E. Cooper,et al.  E. J. Will , 1985 .

[10]  Xiaolin Hu,et al.  Recurrent convolutional neural network for object recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Aboul Ella Hassenian,et al.  Automatic Fruit Image Recognition System Based on Shape and Color Features , 2014, AMLTA.

[12]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[13]  C. Glasbey,et al.  Automatic fruit recognition and counting from multiple images , 2014 .

[14]  Hui Zhao,et al.  Cucumber Detection Based on Texture and Color in Greenhouse , 2017, Int. J. Pattern Recognit. Artif. Intell..

[15]  Hong Cheng,et al.  Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks , 2017, J. Imaging.

[16]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[17]  Tristan Perez,et al.  DeepFruits: A Fruit Detection System Using Deep Neural Networks , 2016, Sensors.

[18]  脇元 修一,et al.  IEEE International Conference on Robotics and Automation (ICRA) におけるフルードパワー技術の研究動向 , 2011 .

[19]  Erich Elsen,et al.  Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.

[20]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[21]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[23]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Yann LeCun,et al.  The mnist database of handwritten digits , 2005 .

[25]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Jürgen Schmidhuber,et al.  An Application of Recurrent Neural Networks to Discriminative Keyword Spotting , 2007, ICANN.

[27]  Laurent Tits,et al.  Automated visual fruit detection for harvest estimation and robotic harvesting , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[28]  Jochen Hemming,et al.  Fruit Detectability Analysis for Different Camera Positions in Sweet-Pepper † , 2014, Sensors.

[29]  Jochen Hemming,et al.  Data synthesis methods for semantic segmentation in agriculture: A Capsicum annuum dataset , 2018, Comput. Electron. Agric..

[30]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[31]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[32]  Luca Maria Gambardella,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification , 2022 .

[33]  Zhen Liu,et al.  Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination , 2018, Sensors.

[34]  Pragati Ninawe A Completion on Fruit Recognition System Using K-Nearest Neighbors Algorithm , 2014 .

[35]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).