Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting
文献类型： 外文期刊
作者： Han, Yanyun 1 ; Wang, Kaiyi 1 ; Yang, Feng 1 ; Pan, Shouhui 1 ; Liu, Zhongqiang 1 ; Zhang, Qiusi 1 ; Zhang, Qi 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing, Peoples R China
2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China
关键词： Prediction of maize cultivar yield; Machine learning; Random forest; Levenberg - Marquardt neural network; Multilayer perceptron neural network; Assessment of varieties
期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：5.6； 五年影响因子：6.3 ）
ISSN： 0168-1923
年卷期： 2024 年 355 卷
页码：
收录情况： SCI
摘要： This study proposed a model that utilized machine learning algorithms to predict the yield of maize (Zea mays L.) cultivars. This will enable the selection of good cultivars with high yields that are suitable for planting in specific areas, such as a district or county. The breeding value of the cultivars and 11 types of time-series meteorological variables were selected as the input parameters of the model. The yield was set as the output parameter of the model. Random forest (RF), Levenberg-Marquardt neural network, and multilayer perceptron neural network algorithms were used to construct the model. The results showed that the RF outperformed the other algorithms in predicting the yield of maize cultivars by achieving the maximum coefficient of determination (R2) of 0.77 and minimal root-mean-square error of 320.25 kg/acre, mean absolute error of 229.84 kg/acre, and mean absolute percentage error of 7.1%. The constructed model can be used to effectively predict the yield of specific varieties to enable the selection of good varieties in specific areas, such as a district or county. A prediction of the yield of a specific maize cultivar in a particular planting environment can have considerable value. It can facilitate the objective identification of better adapting cultivars among farmers and support the precise promotion and planting of cultivars.
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