Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : ?brahim Kahramano?lu, Ezgi Deniz Ülker, Sadık Ülker
DOI :  10.14445/22315381/IJETT-V69I3P205


MLA Style: ?brahim Kahramano?lu, Ezgi Deniz Ülker, Sadık Ülker  "Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants" International Journal of Engineering Trends and Technology 69.3(2021):23-28. 

APA Style:?brahim Kahramano?lu, Ezgi Deniz Ülker, Sadık Ülker. Integrating Support Vector Machine (SVM) Technique and Contact Imaging for Fast Estimation of the Leaf Chlorophyll Contents of Strawberry Plants  International Journal of Engineering Trends and Technology, 69(3),23-28.

Present research was conducted to define the interrelation between the RGB colors of contact imaging and the leaf chlorophyll content of the strawberry plants and to evaluate the expediency of support vector machine (SVM) in the estimation of leaf chlorophyll contents. A closed box was developed with a small hole (8 mm x 8 mm) on, and a red LED source was placed inside the box. A total of 30 leaves were hand collected from 10 different strawberry plantations, and they were placed on the hole one by one. A smartphone was used to capture the contact image of samples and to determine the RGB colors. The destructive determination of the leaf chlorophyll contents was then determined by using standard methods at spectrophotometer. Finally, the RGB color values were treated with a support vector machine for the evaluation of leaf chlorophyll content. In training the data, R-mean, G-mean, B-mean values were used as the input parameters and level of chlorophyll as the output. In support vectors, the model is tested with two different kernels: radial kernel and linear kernel. Results suggested that the estimation of leaf chlorophyll content of strawberry plants through the SVM method could be effective by using RGB colors of contact imaging.

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Contact imaging, precise agriculture, RGB colors, smartphone, support vector machines