Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Agricultural Sciences in China

Volumn 9, Issue 1, 2010, Pages 38-45

  SUO, Xing mei ^a   JIANG, Ying tao ^b   YANG, Mei ^b   LI, Shao kun ^c   WANG, Ke rum ^c   WANG, Chong tao ^c  

^a MINZU UNIVERSITY OF CHINA   (China)

^b Las Vegas   (United States)

^c INSTITUTE OF PLANT PROTECTION   (China)

Author keywords

artificial neural network; cotton plant; image processing; leaf population chlorophyll content; prediction

Indexed keywords

GOSSYPIUM HIRSUTUM;

EID: 77649178041     PISSN: 16712927     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1671-2927(09)60065-1     Document Type: Article

References (20)

1. Aitkenhead M.J., Dalgetty I.A., Mullins C.E., McDonald A., and Strachan N.J.C. Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture 39 (2003) 157-171
2. Chappelle E.W., Kim M.S., and McMurtrey J.E. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment 39 (1992) 239-247
3. Curran P.J., Dungan J.L., Macler B.A., and Plummer S.E. The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment 35 (1991) 69-76
4. Daughtry C.S.T., Walthall C.L., Kim M.S., Brown de Colstoun E., and McMurtrey III J.E. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing Environment 74 (2000) 229-239
5. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification (2001), John Wiley & Sons, New York, USA
6. Ferns D.C., Zara S.J., and Barber J. Application of high resolution spectroradiometry to vegetation. Photogrammetric Engineering and Remote Sensing 50 (1984) 1725-1735
7. Kang T.J., and Kim S.C. Objection evaluation of the trash and color of raw cotton by image processing and neural network. Textile Research Journal 72 (2002) 776-782
8. Kawashima S., and Nakatani M. An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of Botany 81 (1998) 49-54
9. Li S.K., Suo X.M., Bai Z.Y., Qi Z.L., Liu X.H., Gao S.J., and Zhao S.N. The machine recognition for population feature of wheat images based on BP neural network. Agricultural Sciences in China 8 (2002) 885-889
10. Markwell J., Osterman J.C., and Mitchell J.L. Calibration of the minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 16 (2005) 467-472
11. Merzlyak M.N., Gitelson A.A., Chivkunova O.B., and Rakitin V. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum 106 (1999) 135-141
12. Nasr A.H., Darwish A.M., and Shaheen S.I. Use of intensity-hue-saturation (IHS) transformations in change detection of multitemporal remotely sensed data. Proceeding of SPIE (the Society of Photo-Optical Instrumentation Engineers), the 8th International Symposium on Remote Sensing vol. 4541 (2001), Center de Congres, Toulouse, France 318-327
13. Palta J.P. Leaf chlorophyll contents. Remote Sensing Reviews 5 (1990) 207-213 In: Goel N S, Norman J M, eds, Instrumentation for Studying Vegetation Canopies for Remote Sensing in Optical and Thermal Infrared Regions
14. Porra R.J., Thomson W.A., and Kriedmann P.E. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochimica et Biophysica Acta 975 (1989) 384-394
15. Richardson A.D., Duigan S.P., and Berlyn G.P. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist 153 (2002) 185-194
16. Salisbury J.W., Milton N.M., and Walsh P.A. Significance of non-isotropic scattering from vegetation for geobotanical remote sensing. International Journal of Remote Sensing 8 (1987) 997-1009
17. Shahin M.A., Tollner E.W., Gitaitis R.D., Sumner D.R., and Maw B.W. Classification of sweet onions based on internal defects using image processing and neural network techniques. Transactions of the ASAE 45 (2002) 1613-1618
18. Spomer L.A., Smith M., and Sawwan J.S. Rapid nondestructive measurement of chlorophyll content in leaves with nonuniform chlorophyll distribution. Photosynthesis Research 16 (1988) 277-284
19. Wang K.R., Li S.K., Wang C.T., Yang L., Xie R.Z., Gao S.J., and Bai J.H. Acquired chlorophyll concentration of cotton leaves with technology of machine vision. Acta Agronomica Sinica 32 (2006) 34-40 (in Chinese)
20. Wang F.Y., Li S.K., Wang K.R., Sui X.Y., Bai J.H., Cheng B., Liu G.Q., and Tan H.Z. Obtaining information of cotton population chlorophyll by using machine vision technology. Acta Agronomica Sinica in China 33 (2007) 2041-2046 (in Chinese)