Title :
Study on germination of tomato seed based on near-infrared spectroscopy
Author :
Wang Tao ; Wang Xiaofei
Author_Institution :
Beijing Inf. Sci. & Technol. Univ., Beijing, China
Abstract :
Near infrared spectroscopy has been applied to forecast the tomato seed germination rate. It depends on some kinds of ingredient whether the seed can germinate. The spectroscopy data of seed is collected using the diffuse reflectance spectral, and the support vector machine is used to establish the model, so as to identify whether the seed can germinate. Support vector machine, as a new generation of machine learning algorithm, is widely applied to many fields successfully. This paper also points out the problems about future research direction for forecasting tomato seed germination rate based on near-infrared spectroscopy.
Keywords :
agricultural products; biology computing; infrared spectroscopy; support vector machines; diffuse reflectance spectral; machine learning algorithm; near-infrared spectroscopy; support vector machine; tomato seed germination; Approximation algorithms; Conferences; Machine learning algorithms; Pollution measurement; Spectroscopy; Support vector machines; Training; near-infrared spectroscopy; rate of seed germination; support vector machine; tomato;
Conference_Titel :
Electronic Measurement & Instruments (ICEMI), 2013 IEEE 11th International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-0757-1
DOI :
10.1109/ICEMI.2013.6743025