Parviz Ahmadi MOGHADDAM, Mohammadali Haddad DERAFSHI, Vine SHIRZAD

Estimation of single leaf chlorophyll content in sugar beet using machine vision

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.

Anahtar Kelimeler:

Key words: Machine vision, neural network, chlorophyll, sugar beet, variable rate

Estimation of single leaf chlorophyll content in sugar beet using machine vision

Keywords:

Key words: Machine vision, neural network, chlorophyll, sugar beet, variable rate,

PDF

___

Biermacher J, Epplin F, Brorsen B, Solie J, Raun M (2009) Economic feasibility of site-specifi c optical sensing for managing nitrogen fertilizer for growing wheat. Precision Agriculture 10: 213-230.
Chen CT, Chen S, Hsieh KW, Yang HC, Hsiao S, Yang C (2007) Estimation of leaf nitrogen content using artifi cial neural network with cross-learning scheme and signifi cant wavelengths. Transactions of the ASAE 50: 295-301.
Demuth H, Beale M (2000) Neural network toolbox, Version 4. Th e MathWorks, Inc. Natick, MA, USA.
Gautam RK, Panigrahi S (2007) Leaf nitrogen determination of corn plant using aerial images and artifi cial neural networks. Canadian Biosystems Engineering 49: 7.1-7.9.
Gitelson AA, Merzlyak MN (1997) Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18: 2691-2697.
Hassoun MH (1995) Fundamentals of Artifi cial Neural Networks. Cambridge, MA: MIT Press.
Kawashima S, Nakatani M (1998) An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of Botany 81: 49-54.
Koumpouros Y, Mahaman BD, Maliappis M, Passam HC, Sideridis AB, Zorkadis V (2004) Image processing for distance diagnosis in pest management. Computers and Electronics in Agriculture 44: 121-131.
Liangliang J, Chen X, Zhang F, Buerkert A, Romheld V (2004) Use of digital camera to assess nitrogen status on winter wheat in the northern china plain. Journal of Plant Nutrition 27: 441-450.
Miao Y, Mulla DJ, Randall GW, Vetsch AJ, Vintila R (2009) Combining chlorophyll meter readings and high spatial resolution remote sensing images for in season site-specifi c nitrogen management of corn. Precision Agriculture 10: 45-62.
Namrata J, Ray SS, Singh JP, Panigrahy S (2007) Use of hyperspectral data to assess the eff ects of diff erent nitrogen applications on a potato crop. Precision Agriculture 8: 225-239.
Noh H, Zhang Q, Shin B, Han S, Feng L (2006) A neural network model of maize crop nitrogen stress assessment for a multi- spectral imaging sensor. Biosystems Engineering 94: 477-485.
Pagola M, Ruben O, Ignacio I, Humberto B, Edurne B, Pedro AT, Carmen L, Berta L (2009) New method to assess barley nitrogen nutrition status based on image color analysis comparison with SPAD-502. Computers and Electronics in Agriculture 65: 213- 218.
Pydipati R, Burks TF, Lee WS (2006) Identifi cation of citrus disease using color texture features and discriminate analysis. Computers and Electronics in Agriculture 52: 49-59.
Roberts TL (2008) Improving nutrient use effi ciency. Turk J Agric For 32: 177-182.
Shapiro CA (1999) Using a chlorophyll meter to manage nitrogen applications to corn with high nitrate irrigation water. Communication in Soil Science and Plant Analysis 30: 1037- 1049.
Sindir KO, Tekin AB (2002) Economics of variable rate fertilizer application. International Scientifi c Conference 04-06.04, Rousse, Bulgaria.
Wang Y, Wang F, Huang J, Wang X, Liu Z (2009) Validation of artifi cial neural network techniques in the estimation of nitrogen concentration in rape using canopy hyperspectral refl ectance data. International Journal of Remote Sensing 30: 4493-4505.
White JD, Trotter CM, Brown LJ, Scott N (2000) Nitrogen concentration in New Zealand vegetation foliage derived from laboratory and fi eld spectrometry. International Journal of Remote Sensing 21: 2525-2531.
Woebbecke DM, Meyer GE, Bargen KV, Mortensen DA (1995) Color indices for weed identifi cation under various soil, residue and lighting conditions. Transactions of the ASAE 38: 259-269.