Online learning method for predicting air environmental information used in agricultural robots
Abstract
Keywords: online learning method, conventional neural network, real-time prediction, air environmental information
DOI: 10.25165/j.ijabe.20241705.7972
Citation: Wang Y T, Li M Z, Ji R H, Wang M J, Zhang Y, Zheng L H. Online learning method for predicting air environmental information used in agricultural robots. Int J Agric & Biol Eng, 2024; 17(5): 206-212.
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Hamrita T K, Conway R H. First order dynamics approaching of broiler chicken deep body temperature response to step changes in ambient temperature. Int J Agric & Biol Eng, 2017; 10(4): 13–21.
Fu X, Shen W Z, Yin Y L, Zhang Y, Yan S C, Kou T L, et al. Remote monitoring system for livestock environmental information based on LoRa wireless ad hoc network technology. Int J Agric & Biol Eng, 2022; 15(4): 79–89.
Boomgard-Zagrodnik J P, Brown D J. Machine learning imputation of missing Mesonet temperature observations. Computers and Electronics in Agriculture, 2022; 192: 106580.
Wang Y T, Li M Z, Ji R H, Wang M J, Zhang Y, Zheng L H. A convolutional operation-based online computation offloading approach in wireless powered multi-access edge computing networks. Computers and Electronics in Agriculture, 2022; 197: 106967.
Orabona F. A modern introduction to online learning. arXiv, In press. 2019; arXiv: 1912.13213.
Ross S, Gordon G J, Bagnell J A. A reduction of imitation learning and structured prediction to no-regret online learning. Journal Of Machine Learning Research, Aistats, 2011; pp.627–635.
Breiman L. Bagging predictors. Machine Learning, 1996; 24: 123–140.
Rachman T. Support vector regression machines. Angew. Chemie Int. Ed. 2018; 6(11): 951–952.
Tomassetti B, Verdecchia M, Giorgi F. NN5: A neural network based approach for the downscaling of precipitation fields - Model description and preliminary results. Journal of Hydrology, 2009; 367: 14–26.
Deznabi I, Arabaci B, Koyuturk M, Tastan O. DeepKinZero: Zero-shot learning for predicting kinase-phosphosite associations involving understudied kinases. Bioinformatics, 2020; 36(12): 3652–3661.
Shi W Z, Caballero J, Theis F. Huszar A, Aitken A, Ledig C, et al. Is the deconvolution layer the same as a convolutional layer? 2016; arXiv, In Press. arXiv: 1609.07009.
Wang Y T, Li M Z, Ji R H, Wang M J, Zhang Y, Zheng L H. Novel encoder for ambient data compression applied to microcontrollers in agricultural robots. Int J Agric & Biol Eng, 2022; 15(4): 197–204.
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, et al. Attention is all you need. In: NIPS'17: Procedings of the 31st International Conference on Neural Information Processing Systems, 2017; pp.6000–6010. doi: 10.5555/3295222.3295349.
Lillicrap T P, Hunt J J, Pritzel A, Heess N, Erez T, Tassa Y, et al. Continuous control with deep reinforcement learning. In: 4th Int. Conf. Learn. Represent. ICLR 2016 - Conf. Track Proc, 2016.
Montague P R. Reinforcement learning: An introduction, by Sutton, R. S. and Barto, A. G. Trends in Cognitive Sciences, 1999; 3(9): 360.
Jarrett K, Kavukcuoglu K, Ranzato M A, LeCun Y. What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, 2009; pp.2146–2153. doi: 10.1109/ICCV.2009.5459469.
Batal I, Hauskrecht M. Constructing classification features using minimal predictive patterns. In: CIKM '10: Proceedings of the 19th ACM International Conference on Information, 2010; pp.869–878. doi: 10.1145/1871437.1871549.
Wang Y T, Li M Z, Ji R H, Wang M J, Zhang Y, Zheng L H. Construction of complex features for predicting soil total nitrogen content based on convolution operations. Soil and Tillage Research, 2021; 213: 105109.
Jin Z W, Shang J X, Zhu Q W, Ling C, Xie W, Qiang B H. RFRSF: Employee turnover prediction based on random forests and survival analysis. In: Web Information Systems Engineering - WISE 2020, 2020; pp.503–515. doi: 10.1007/978-3-030-62008-0_35.
Gonzalez-Mora A F, Rousseau A N, Larios A D, Godbout S, Fournel S. Assessing environmental control strategies in cage-free aviary housing systems: Egg production analysis and Random Forest modeling. Computers and Electronics in Agriculture, 2022; 196: 106854.
Wang Y T, Li M Z, Ji R H, Wang M J, Zheng L H. A deep learning-based method for screening soil total nitrogen characteristic wavelengths. Computers and Electronics in Agriculture, 2021; 187: 106228.
Li F, Shirahama K, Nisar M A, Koping L, Grzegorzek M. Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors, 2018; 18(2): 679.
Hu J L, Lu J W, Tan Y-P, Zhou J. Deep transfer metric learning. IEEE Transactions on Image Processing, 2016; 25(12): 5576–5588.
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