Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John's Wort Leaves

被引：13

作者：

Odabas, Mehmet Serhat ^{[1
]}

Kayhan, Gokhan ^{[2
]}

Ergun, Erhan ^{[2
]}

Senyer, Nurettin ^{[2
]}

机构：

[1] Ondokuz Mayis Univ, Bafra Vocat Sch, TR-55400 Bafra, Samsun, Turkey

[2] Ondokuz Mayis Univ, Fac Engn, Dept Comp Engn, TR-55400 Bafra, Samsun, Turkey

来源：

COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS | 2016年 / 47卷 / 02期

关键词：

Artificial neural network; chlorophyll concentration index; Hypericum perforatum L; modeling; precision agriculture; SYSTEM;

D O I：

10.1080/00103624.2015.1104342

中图分类号：

S3 [农学（农艺学）];

学科分类号：

0901 ;

摘要：

This research investigates and compares artificial neural network and multiple linear regression for predicting the chlorophyll concentration index of Saint John's wort leaves (Hypericum perforatum L.). Plants were fertilized with 0, 30, 60, 90, and 120kgha(-1) nitrogen [34% nitrogen ammonium nitrate (NH4NO3)]. Chlorophyll concentration index of each leaf was measured using SPAD meter. Afterwards, rgb (red, green, and blue color) values of all leaf images were determined by image processing. Values obtained were modeled using both multiple regression analysis and artificial neural networks. Using multiple regression analysis R-2 values were between 0.61 and 0.97. Coefficient of determination values (R-2) using artificial neutral network values were found to be 0.99. Artificial neutral network modeling successfully described the relationship between actual chlorophyll concentration index values and predicted chlorophyll concentration index values.

引用

页码：237 / 245

页数：9

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