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All issues Volume 142 (2024) BIO Web Conf., 142 (2024) 01004 References
Open Access
Issue
BIO Web Conf.
Volume 142, 2024
2024 International Symposium on Agricultural Engineering and Biology (ISAEB 2024)
Article Number 01004
Number of page(s) 5
Section Agricultural Economic Engineering and Market Management
DOI https://doi.org/10.1051/bioconf/202414201004
Published online 21 November 2024
  • Jin, X.-B. et al. Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability 12, 1433 (2020). [CrossRef] [Google Scholar]
  • Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D.& Traore, D. Deep learning for precision agriculture: A bibliometric analysis. Intell. Syst. Appl. 16, 200102 (2022). [Google Scholar]
  • Kashyap, P. K., Kumar, S., Jaiswal, A., Prasad, M.& Gandomi, A. H. Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network. IEEE Sens. J. 21, 17479–17491 (2021). [Google Scholar]
  • Zhao, W., Yamada, W., Li, T., Digman, M.& Runge, T. Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection. Remote Sens. 13, 23 (2021). [Google Scholar]
  • Khanna, A.& Kaur, S. Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Comput. Electron. Agric. 157, 218–231 (2019). [CrossRef] [Google Scholar]
  • Maes, W. H. & Steppe, K. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture. Trends Plant Sci. 24, 152–164 (2019). [CrossRef] [Google Scholar]
  • Chu, Z.& Yu, J. An end-to-end model for rice yield prediction using deep learning fusion. Comput. Electron. Agric. 174, 105471 (2020). [CrossRef] [Google Scholar]
  • Wang, A. X., Tran, C., Desai, N., Lobell, D.& Ermon, S.Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data. in Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies 1–5 (Association for Computing Machinery, 2018). doi:10.1145/3209811.3212707. [Google Scholar]
  • Nevavuori, P., Narra, N.& Lipping, T. Crop yield prediction with deep convolutional neural networks. Comput. Electron. Agric. 163, 104859 (2019). [CrossRef] [Google Scholar]
  • Maimaitijiang, M. et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens. Environ. 237, 111599 (2020). [CrossRef] [Google Scholar]
  • Hopfield, J. J. Artificial neural networks. IEEE Circuits Devices Mag. 4, 3–10 (1988). [CrossRef] [Google Scholar]
  • Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018). [CrossRef] [Google Scholar]
  • Li, Z., Liu, F., Yang, W., Peng, S.& Zhou, J. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999–7019 (2022). [Google Scholar]
  • Salehinejad, H., Sankar, S., Barfett, J., Colak, E.& Valaee, S. Recent Advances in Recurrent Neural Networks. Preprint at https://doi.org/10.48550/arXiv.1801.01078 (2018). [Google Scholar]
  • Ruder, S. An overview of gradient descent optimization algorithms. Preprint at https://doi.org/10.48550/arXiv.1609.04747 (2017). [Google Scholar]
  • Bottou, L. Stochastic Gradient Descent Tricks. in Neural Networks: Tricks of the Trade: Second Edition (eds. Montavon, G., Orr, G. B. & Müller, K. - R.) 421–436 (Springer, 2012). doi:10.1007/978-3-642-35289-8_25. [CrossRef] [Google Scholar]
  • Andrychowicz, M. et al. Learning to learn by gradient descent by gradient descent. in Advances in Neural Information Processing Systems vol. 29 (Curran Associates, Inc., 2016). [Google Scholar]
  • O’Shea, K.& Nash, R. An Introduction to Convolutional Neural Networks. Preprint at https://doi.org/10.48550/arXiv.1511.08458 (2015). [Google Scholar]
  • Dai, D. An Introduction of CNN: Models and Training on Neural Network Models. in 2021 International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) 135–138 (2021). doi:10.1109/ICBAR55169.2021.00037. [Google Scholar]
  • Sherstinsky, A. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Phys. Nonlinear Phenom. 404, 132306 (2020). [CrossRef] [Google Scholar]
  • Smagulova, K.& James, A. P. A survey on LSTM memristive neural network architectures and applications. Eur. Phys. J. Spec. Top. 228, 2313–2324 (2019). [CrossRef] [Google Scholar]
  • Dey, R.& Salem, F. M. Gate-variants of Gated Recurrent Unit (GRU) neural networks. in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS) 1597–1600 (2017). doi:10.1109/MWSCAS.2017.8053243. [Google Scholar]
  • He, K., Zhang, X., Ren, S.& Sun, J. Deep Residual Learning for Image Recognition. (2015). [Google Scholar]
  • Ju, xiangyang et al. Benchmarking GPU and TPU Performance with Graph Neural Networks. Preprint at https://doi.org/10.48550/arXiv.2210.12247 (2022). [Google Scholar]
  • Jouppi, N. P. et al. In-Datacenter Performance Analysis of a Tensor Processing Unit. in Proceedings of the 44th Annual International Symposium on Computer Architecture 1–12 (Association for Computing Machinery, 2017). doi:10.1145/3079856.3080246. [Google Scholar]
  • Wang, Y. E., Wei, G.-Y. & Brooks, D. Benchmarking TPU, GPU, and CPU Platforms for Deep Learning. Preprint at https://doi.org/10.48550/arXiv.1907.10701 (2019). [Google Scholar]

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