Fruit recognition from images using deep learning

[1] S. Arivazhagan, N. Shebiah, S. Nidhyanandhan, L.Ganesan, Fruit recognition using color and texture features, Journal of Emerging Trends in Computing and Information Sciences, 1, 2 (2010) 90–94. ⇒29Search in Google Scholar

[2] S. Bargoti, J. Underwood, Deep fruit detection in orchards, IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 3626–3633. ⇒2810.1109/ICRA.2017.7989417Search in Google Scholar

[3] R. Barth, J. Ijsselmuiden, J. Hemming, E. Van Henten, Data synthesis methods for semantic segmentation in agriculture: A Capsicum annuum dataset, Computers and Electronics in Agriculture144 (2018) 284–296. ⇒2910.1016/j.compag.2017.12.001Search in Google Scholar

[4] T. F. Chan, L. Vese, Active contours without edges. IEEE Trans. Image Process10, (2001) 266–277. ⇒2910.1109/83.90229118249617Search in Google Scholar

[5] H. Cheng, L. Damerow, Y. Sun, M. Blanke, Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks, Journal of Imaging, 3, 1 (2017) 6. ⇒2910.3390/jimaging3010006Search in Google Scholar

[6] D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber, Flexible, high performance convolutional neural networks for image classification, Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, pp. 1237–1242, AAAI Press, 2011. ⇒30Search in Google Scholar

[7] D. C. Cireşsan, U. Meier, J. Schmidhuber, Multi-column deep neural networks for image classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Providence, pp. 3642–3649, 2012. ⇒3010.1109/CVPR.2012.6248110Search in Google Scholar

[8] D. Clevert, T. Unterthiner, S. Hochreiter, Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) CoRR abs/1511.07289, 2015. ⇒40Search in Google Scholar

[9] J. Hemming, J. Ruizendaal, J. W. Hofstee. E J. Van Henten, Fruit detectability analysis for different camera positions in sweet-pepper Sensors14, 4 (2014) 6032–6044. ⇒2910.3390/s140406032402969224681670Search in Google Scholar

[10] K. Kapach, E. Barnea, R. Mairon, Y. Edan, O. Ben-Shahar, Computer vision for fruit harvesting robots state of the art and challenges ahead, Journal of Imaging3, 1 (2017) 4–34. ⇒2910.1504/IJCVR.2012.046419Search in Google Scholar

[11] M. Liang, X. Hu, Recurrent convolutional neural network for object recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston. 2015. pp. 3367–3375. ⇒31Search in Google Scholar

[12] D. Mumford, J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math. 42 (1989) 577-685. ⇒2910.1002/cpa.3160420503Search in Google Scholar

[13] P. Ninawe, S. Pandey, A completion on fruit recognition system using k-nearest neighbors algorithm, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)3, 7 (2014) 2352–2356. ⇒29Search in Google Scholar

[14] S. Puttemans, Y. Vanbrabant, L. Tits, T. Goedem, Automated visual fruit detection for harvest estimation and robotic harvesting, Sixth International Conference on Image Processing Theory, Tools and Applications, 2016. ⇒2910.1109/IPTA.2016.7820996Search in Google Scholar

[15] M, Rahnemoonfar, C. Sheppard, Deep count: fruit counting based on deep simulated learning, Sensors17, 4 (2017) 905. ⇒2910.3390/s17040905542682928425947Search in Google Scholar

[16] S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 2015, 91–99. ⇒29Search in Google Scholar

[17] I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez & C. McCool, DeepFruits: A fruit detection system using deep neural networks, Sensors16, 8 (2016) 1222. ⇒2810.3390/s16081222Search in Google Scholar

[18] J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks61 (2015) 85–117. ⇒3010.1016/j.neunet.2014.09.00325462637Search in Google Scholar

[19] Y. Song, C. Glasbey, G. Horgan, G. Polder, J. A. Dieleman, G. Van Der Heijden, Automatic fruit recognition and counting from multiple images, Biosystems Engineering118 (2)14 203–215. ⇒2810.1016/j.biosystemseng.2013.12.008Search in Google Scholar

[20] J. T. Springenberg, A. Dosovitskiy, T. Brox, M. A. Riedmiller, Striving for simplicity: The all convolutional net, CoRR abs/1412.6806, 2014. ⇒31, 40Search in Google Scholar

[21] R. K. Srivastava, K. Greff, J. Schmidhuber, Training very deep networks, advances in neural information processing systems, Twenty-Eight International Conference on Neural Information Processing Systems, Montreal, Canada, 2015, pp. 2377–2385. ⇒30Search in Google Scholar

[22] J. Xiong, Z. Liu, R. Lin, R. Bu, Z. He, Z. Yang, C. Liang, Green grape detection and picking-point calculation in a night-time natural environment using a charge-coupled device (CCD) vision sensor with artificial ullumination, Sensors18, 4, (2018) 969. ⇒2910.3390/s18040969Search in Google Scholar

[23] H. M. Zawbaa, M. Abbass, M. Hazman, A. E. Hassenian, Automatic fruit image recognition system based on shape and color features, in: Advanced Machine Learning Technologies and Applications. (eds.: Hassanien A. E., Tolba M. F., Taher Azar A.) AMLTA 2014. Series: Communications in Computer and Information Science vol. 488, 2014, 278–290. ⇒2910.1007/978-3-319-13461-1_27Search in Google Scholar

[24] D. Li, H. Zhao, X. Zhao, Q. Gao, L. Xu, Cucumber detection based on texture and color in greenhouse, International Journal of Pattern Recognition and Arti cial Intelligence31 1754016 (2017) 17 pag. ⇒2910.1142/S0218001417540167Search in Google Scholar

[25] Convolution in Mathematics. https://en.wikipedia.org/wiki/Convolution. Last visited on 26.05.2018 ⇒35Search in Google Scholar

[26] Deep Learning article on Wikipedia. https://en.wikipedia.org/wiki/Deep_learning. Last visited on 05.05.2018 ⇒30Search in Google Scholar

[27] TensorFlow. https://www.tensorflow.org. Last visited on 05.05.2018 ⇒ 27, 35, 37Search in Google Scholar

[28] MNIST. http://yann.lecun.com/exdb/mnist. Last visited on 05.05.2018 ⇒30Search in Google Scholar

[29] CIFAR-10 and CIFAR-100 Datasets. https://www.cs.toronto.edu/~kriz/cifar.html. Last visited on 05.05.2018 ⇒27, 31Search in Google Scholar

[30] Fruits 360 Dataset on GitHub. https://github.com/Horea94/Fruit-Images-Dataset. Last visited on 05.05.2018 ⇒26, 32Search in Google Scholar

[31] Fruits 360 Dataset on Kaggle. https://www.kaggle.com/moltean/fruits. Last visited on 28.05.2018 ⇒26, 32Search in Google Scholar

[32] Britta O’Boyle, Chris Hall, What is Google Lens and how do you use it? Last visited on 05.05.2018 ⇒27Search in Google Scholar

[33] Google Lens on Wikipedia, https://en.wikipedia.org/wiki/Google_Lens. Last visited on 05.05.2018 ⇒27Search in Google Scholar