基于人工智能的作物产量预测模型的性能研究

Nantinee Soodtoetong, Eakbodin Gedkhaw, Montean Rattanasiriwongwut
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引用次数: 1

摘要

通过比较基于人工智能的作物产量预测模型的精度,介绍了作物产量预测模型的性能。为了比较误差值,研究人员使用了2个统计值,即多重决定系数R2和均方根误差RMSE。R2值最高的模型和RMSE值最低的模型将是合适的模型。本文中用于比较的模型包括线性回归、决策树回归、k近邻回归、支持向量回归和多层感知机网络。学习数据集来自2004-2018年泰国荔枝种植面积和产量的调查,以及2019年的数据集,以测试模型的性能。结果表明,支持向量回归的预测准确率最高,其次是多层感知器网络,其次是线性回归,其次是决策树回归,最后是k近邻回归。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Performance of Crop Yield Forecasting Model based on Artificial Intelligence
This paper presents the performance of crop yield forecasting model by comparing the accuracy of the crop yield forecasting models based on artificial intelligence. To compare the error values, researchers use 2 statistical values which are the Coefficient of Multiple Determination (R2) and the Root Mean Square Error (RMSE). The model has the highest R2 value and the lowest RMSE value will be the appropriate model. The models used for comparison in this paper include Linear Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Support Vector Regression, and Multilayer Perceptron Network. The learning dataset comes from the survey of planting area and yield of lychee in Thailand during 2004-2018 and dataset in 2019 to test the performance of the model. The results show that Support Vector Regression has the highest accuracy in forecasting, followed by Multilayer perceptron network, Linear Regression, Decision Tree Regression and k-Nearest Neighbors Regression, respectively.
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