Multilayer Perceptron Neural Network Approach to Estimate Chlorophyll Concentration Index of Lettuce (Lactuca sativa L.)

被引:23
作者
Odabas, Mehmet Serhat [1 ]
Simsek, Halis [2 ]
Lee, Chiwon W. [3 ]
Iseri, Ismail [4 ]
机构
[1] Ondokuz Mayis Univ, Bafra Vocat Sch, Samsun, Turkey
[2] North Dakota State Univ, Dept Agr & Biosyst Engn, 1221 Albrecht Blvd, Fargo, ND 58102 USA
[3] North Dakota State Univ, Dept Plant Sci, 1221 Albrecht Blvd, Fargo, ND 58102 USA
[4] Ondokuz Mayis Univ, Dept Comp Engn, Samsun, Turkey
关键词
Artificial neural network; chlorophyll; leaf; lettuce; modeling; SPAD; REFLECTANCE; LEAVES; YIELD; IDENTIFICATION; ALGORITHM; QUALITY;
D O I
10.1080/00103624.2016.1253726
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Nitrogen is an essential nutrient for greenhouse-grown lettuce (Lactuca sativa L.); however, excessive nutrient availability causes disease and detrimental effects on the leaf and root development. In this study, nitrogen content of the lettuce leaves was estimated by determining the chlorophyll concentrations of the leaves using image processing technique. The Hoagland solution was used as a fertilizer in five different doses (control, quarter of the solution, half of the solution, standard solution, and two times more of the solution). Multilayer perceptron neural network (MLPNN) model was developed based on the red, green, and blue components of the color image captured to estimate chlorophyll content and chlorophyll concentration index (SPAD values). According to the obtained results, the MLPNN model was capable of estimating the lettuce leaf chlorophyll content with a reasonable accuracy. The coefficient of determination was 0.98, and mean square error was 0.006 in validation process.
引用
收藏
页码:162 / 169
页数:8
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