A novel approach to detect and classify fruits using ShuffleNet V2

In the proposed context, we show an identification and classification approach of organic products between 41 unique classes. We have utilized a pre-trained Convolutional Neural Network design, the ShuffleNet V2, chosen as for the proficient presentation extent of building convolutional blocks at ease, by using more feature channels. The model, when tried on the proposed dataset, accomplished a test accuracy of 96.24% accordingly making a stride further in the exploration proposed by past authors surveying the organic product detection via Convolutional learning and feature re-usability technique. The outcomes are assessed utilizing various assessment parameters, like Precision, Sensitivity, F-Score, and ROC score. Moreover, a visual of the predicted images was performed to anticipate the evaluation.

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

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

[3]  Mihai Oltean,et al.  Fruit recognition from images using deep learning , 2017, Acta Universitatis Sapientiae, Informatica.

[4]  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).

[5]  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.

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

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Xiangyu Zhang,et al.  ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.

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

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

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

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[13]  Tristan Perez,et al.  Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction , 2014, CLEF.

[14]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[15]  Bankim Patel,et al.  Machine Vision based Fruit Classification and Grading - A Review , 2017 .

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