Online learning method for predicting air environmental information used in agricultural robots
Keywords:
online learning method, conventional neural network, real-time prediction, air environmental informationAbstract
Air environmental information plays an important role during plant growth and reproduction, prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make a timely decision. For efficiency, an online learning method for predicting air environmental information was presented in this work. This method combines the advantages of convolutional neural network (CNN) and experience replay technique: CNN is used to extract features from raw data and predict atmospheric environmental information, experience replay technique can store environmental data over some time and update the hyperparameters of CNN. To validate the effects of this method, this online method was compared with three different predictive methods (including random forest, multi-layer perceptron, and support vector regression) using a public dataset (Jena). According to results, a suitable sample sequence size (e.g., 16) has a smaller number of training sessions and stable results, a larger replay memory size (e.g., 200) can provide enough samples to capture useful features, and 6 d of historical information is the best setting for training predictor. Compared with traditional methods, the method proposed in this study is the only method applied for various conditions. Keywords: online learning method, convolutional 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.References
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[3] Boomgard-Zagrodnik J P, Brown D J. Machine learning imputation of missing Mesonet temperature observations. Computers and Electronics in Agriculture, 2022; 192: 106580.
[4] 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.
[5] Orabona F. A modern introduction to online learning. arXiv, In press. 2019; arXiv: 1912.13213.
[6] 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.
[7] Breiman L. Bagging predictors. Machine Learning, 1996; 24: 123–140.
[8] Rachman T. Support vector regression machines. Angew. Chemie Int. Ed. 2018; 6(11): 951–952.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] Montague P R. Reinforcement learning: An introduction, by Sutton, R. S. and Barto, A. G. Trends in Cognitive Sciences, 1999; 3(9): 360.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
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[21] 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.
[22] 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.
[23] 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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Published
2024-11-08
How to Cite
Wang, Y., Li, M., Ji, R., Wang, M., Zhang, Y., & Zheng, L. (2024). Online learning method for predicting air environmental information used in agricultural robots. International Journal of Agricultural and Biological Engineering, 17(5), 206–212. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7972
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Information Technology, Sensors and Control Systems
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