Turkish Journal of Agriculture and Forestry




Estimating crop nitrogen status accurately during side-dressing operations is essential for effective management of site-specific nitrogen applications. Variable rate technology (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 (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 (R2) and mean square error (MSE) between the estimated and the measured SPAD values, which were obtained from validation tests, appeared to be 0.94 and 0.006, respectively.


Machine vision, neural network, chlorophyll, sugar beet, variable rate

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