- Turkish Journal of Agriculture and Forestry
- Volume:35 Issue:6
- Estimation of single leaf chlorophyll content in sugar beet using machine vision
Estimation of single leaf chlorophyll content in sugar beet using machine vision
Authors : Parviz Ahmadi MOGHADDAM, Mohammadali Haddad DERAFSHI, Vine SHIRZAD
Pages : 563-568
View : 10 | Download : 9
Publication Date : 2011-12-01
Article Type : Research Paper
Abstract :Estimating crop nitrogen status accurately during side-dressing operations is essential for effective management of site-specific nitrogen applications. Variable rate technology insert ignore into journalissuearticles values(VRT); is one of the major operations in precision agriculture to reduce environmental risks and increase fertilizer use efficiency. In the present study, color image analysis was performed to estimate sugar beet leaf chlorophyll status. The experiment was carried out in a phytotron and nitrogen was applied at 6 levels to the sugar beet grown in pots. Chlorophyll level of the leaves was measured by a SPAD-502 chlorophyll meter. To estimate chlorophyll status, a neural-network model was developed based on the RGB insert ignore into journalissuearticles values(red, green, and blue); components of the color image captured with a conventional digital camera. The results showed that the neural network model is capable of estimating the sugar beet leaf chlorophyll with a reasonable accuracy. The coefficient of determination insert ignore into journalissuearticles values(R2); and mean square error insert ignore into journalissuearticles values(MSE); between the estimated and the measured SPAD values, which were obtained from validation tests, appeared to be 0.94 and 0.006, respectively.Keywords : Key words Machine vision, neural network, chlorophyll, sugar beet, variable rate