SUPERIORITY OF ARTIFICIAL NEURAL NETWORK MODEL OVER MULTIPLE LINEAR REGRESSION MODEL FOR PREDICTING BROCCOLI YIELD

被引：0
|
作者：
Mishra, Minakshi [1 ]
Thakur, Ratan Kumar [1 ]
机构：
[1] Babasaheb Bhimrao Ambedkar Univ, Dept Stat, Lucknow 226025, Uttar Pradesh, India
来源：
INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES | 2020年 / 16卷 / 02期
关键词：
Broccoli; Artificial neural networks; Multiple linear regression; Mean absolute error;
D O I：
暂无
中图分类号：
S [农业科学];
学科分类号：
09 ;
摘要：
The present study aimed at the comparison of Artificial Neural Networks and Multiple Linear Regression models for broccoli yield prediction. In this study, the data concerning about broccoli yield and its biological parameters i.e. plant height, number of leaves per plant, length of leaves, width of leaves and stem diameter were taken. Artificial Neural Networks and Multiple Linear Regression models were fitted using the results of experiments to predict total yield of broccoli crop. The performance of Artificial Neural Networks with 10 hidden neurons was found to be more accurate with high value of R-2 (0.999) and low values of root mean squared error, mean squared error, mean absolute error, mean absolute percentage error and percentage forecast error than multiple linear regression prediction.
引用
收藏
页码：757 / 762
页数：6
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