Online learning method for predicting air environmental information used in agricultural robots
文献类型： 外文期刊
作者： Wang, Yueting 1 ; Li, Minzan 2 ; Ji, Ronghua 2 ; Wang, Minjun 2 ; Zhang, Yao 2 ; Zheng, Lihua 2 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China
2.China Agr Univ, Key Lab Smart Agr Syst, Minist Educ, Beijing 100083, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Key Lab Agr Sensors, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
关键词： online learning method; convolutional neural network; real-time prediction; air environmental information
期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.4 ）
ISSN： 1934-6344
年卷期： 2024 年 17 卷 5 期
页码：
收录情况： SCI
摘要： Air environmental information plays an important role during plant growth and reproduction, and prompt and accurate prediction of atmospheric environmental data is helpful for agricultural robots to make timely decisions. In the interest of 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, while 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 the 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 that can be applied for various conditions.
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