Journal of Food, Agriculture and Environment

A new method in assessing sugar beet leaf nitrogen status through color image processing and artificial neural network

Author(s):

Parviz Ahmadi Moghaddam ^1*, Mohammadali Hadad Derafshi ¹, Mahrokh Shayesteh ²

Recieved Date: 2009-12-15, Accepted Date: 2010-04-04

Abstract:

Accurate estimation of crop nitrogen status is essential for effective management of nitrogen applications in precision agriculture. Excessive nitrogen application pollutes underground and running water what leads to serious environmental problems. Variable rate technology for fertilizer application has been developed to reduce environmental risks and increase fertilizer use efficiency. For precise application of this technology, it requires online determination of plant nutrient status in the field. In this study, for the estimation of sugar beet leaf nitrogen, status color image analysis was performed for the first time. The experiment was carried out in a phytotron and nitrogen was applied at six levels to the sugar beet grown in pots. Leaf nitrogen concentration was measured by a SPAD-502 chlorophyll meter. For nitrogen status estimation, a neural-network model and linear regression model were developed based on red (R), green (G) and blue (B) components of the color image captured with a conventional digital camera. The obtained results demonstrated that the neural network model is capable of sugar beet leaf nitrogen estimation with 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, were 0.94 and 0.006, respectively. The results also indicated that the neural network model was performed with higher accuracy than the linear regression model, with R² = 0.88.

Keywords:

Chlorophyll, image processing, neural network, nitrogen, precision agriculture, sugar beet, variable rate

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Journal: Journal of Food, Agriculture and Environment
Year: 2010
Volume: 8
Issue: 2
Category: Agriculture
Pages: 485-489

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