Photosynthetica 2019, 57(2):388-398 | DOI: 10.32615/ps.2019.046

Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light

P. JAGAN MOHAN, S. DUTTA GUPTA
Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur 721302, India

The present study describes a new imaging method to acquire rice leaf images under field conditions using a smartphone and modeling approaches to retrieve the leaf chlorophyll (Chl) content from digitized images. Pearson's correlation of image-based color indices of the relative Chl content measured with Soil Plant Analysis Development (SPAD) indicated the suitability of the color models RGB, rgb, and DGCI-rgb. Among the linear regression models, the models based on mean brightness ratio (rgb) alone or in combination with a dark green color index (DGCI-rgb) show a good correlation between the predicted Chl content and relative Chl content. A feed-forward backpropagation-type network was also developed following the optimization of hidden neurons, training, and transfer functions. The predicted Chl contents showed a good correlation with SPAD values. Compared to the linear regression model, the developed artificial neural network model was found to be more efficient in predicting the Chl content, particularly with RGB index.

Keywords: color index; Oryza sativa; RGB color space.

Received: May 10, 2018; Accepted: October 22, 2018; Prepublished online: February 12, 2019; Published: May 16, 2019Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
MOHAN, P., & GUPTA, S. (2019). Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light. Photosynthetica, 57(2), 388-398. doi: 10.32615/ps.2019.046.
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)

    • Open full article

    Supplementary files

    Download file1945 Table_S3.docx

    File size: 11.31 kB

    Download file1945 Table_S2_.docx

    File size: 11.63 kB

    Download file1945 Table_S1.docx

    File size: 21.18 kB

    Download file1945 Fig._S1.tif

    File size: 1.77 MB

    Download file1945 Fig._S3.tif

    File size: 3.04 MB

    Download file1945 Fig._S2.tif

    File size: 7.7 MB

    References

    1. Agarwal A., Dutta Gupta S.: Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features. - Comput. Electron. Agr. 152: 281-289, 2018. Go to original source...
    2. Cabangon R.J., Castillo E.G., Tuong T.P.: Chlorophyll meter-based nitrogen management of rice growing under alternate wetting and drying irrigation. - Field Crop. Res. 121: 136-146, 2011. Go to original source...
    3. Cassol D., De Silva F.S.P., Falqueto A.R., Bacarin M.A.: An evaluation of non-destructive methods to estimate total chlorophyll content. - Photosynthetica 46: 634-636, 2008. Go to original source...
    4. Chen P., Haboudane D., Tremblay N. et al.: New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. - Remote Sens. Environ. 114: 1987-1997, 2010.
    5. Confalonieri R., Foi M., Casa R. et al.: Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods. - Comput. Electron. Agr. 96: 67-74, 2013. Go to original source...
    6. Curran P.J., Dungan J.L., Gholz H.L.: Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. - Tree Physiol. 7: 33-48, 1990. Go to original source...
    7. Demuth H., Beale M.: Neural Network Toolbox for Use with MATLAB. The MathWorks, Inc, Natick, USA, 1993.
    8. Dutta Gupta S., Ibaraki Y., Pattanayak A.K.: Development of a digital image analysis method for real time estimation of chlorophyll content in micropropagated potato plants. - Plant Biotechnol. Rep. 7: 91-97, 2013. Go to original source...
    9. Dutta Gupta S., Ibaraki Y., Trivedi P.: Applications of RGB colour imaging in plants. - In: Dutta Gupta S., Ibaraki Y. (ed.): Plant Image Analysis: Fundamentals and Applications. Pp. 41-62. CRC Press, New York 2014. Go to original source...
    10. Dutta Gupta S., Pattanayak A.K.: Intelligent image analysis (IIA) using artificial neural network (ANN) for non-invasive estimation of chlorophyll content in micropropagated plants of potato. - In Vitro Cell. Dev.-Pl. 53: 520-526, 2017.
    11. Errecart M.P., Agnusdei M.G., Lattanzi, F.A., Marino M.A.: Leaf nitrogen concentration and chlorophyll meter readings as predictors of tall fescue nitrogen status. - Field Crop. Res. 129: 46-58, 2012. Go to original source...
    12. Friedrichs A., Busch J.A., van der Woerd H.J., Zielinski, O.: SmartFluo: a method and affordable adapter to measure chlorophyll a fluorescence with smartphones. - Sensors-Basel 17: 678, doi: 10.3390/s17040678, 2017. Go to original source...
    13. Gitelson A.A., Kaufman Y.J., Stark R., Rundquist, D.: Novel algorithms for remote estimation of vegetation fraction. - Remote Sens. Environ. 80: 76-87, 2002. Go to original source...
    14. Goetz A.F.H., Rock B.N., Rowan L.C.: Remote sensing for exploration: an overview. - Econ. Geol. 78: 573-590, 1983. Go to original source...
    15. Gómez-Robledo L., López-Ruiz N., Melgosa M. et al.: Using the mobile phone as Munsell soil-colour sensor: an experiment under controlled illumination conditions. - Comput. Electron. Agr. 99: 200-208, 2013. Go to original source...
    16. Gong A., Yu J., He Y., Qiu, Z.: Citrus yield estimation based on images processed by an android mobile phone. - Biosyst. Eng. 115: 162-170, 2013. Go to original source...
    17. Hu H., Zhang J., Sun X., Zhang, X.: Estimation of leaf chlorophyll content of rice using image colour analysis. - Can. J. Remote Sens. 39: 185-190, 2014.
    18. Jinwen, L.: Determination of canopy's average SPAD readings based on the analysis of digital images. - Agrotechnol. 3: 121, doi:10.4172/2168-9881.1000121, 2014. Go to original source...
    19. Karcher D.E., Richardson M.D.: Quantifying turfgrass color using digital image analysis. - Crop Sci. 43: 943-951, 2003. Go to original source...
    20. Kawashima S., Nakatani M.: An algorithm for estimating chlorophyll content in leaves using a video camera. - Ann. Bot.-London 81: 49-54, 1998. Go to original source...
    21. Krogh A.: What are artificial neural networks? - Nat. Biotechnol. 26: 195-197, 2008. Go to original source...
    22. Lee K.J., Lee B.W.: Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. - Eur. J. Agron. 48: 57-65, 2013. Go to original source...
    23. Li F., Mistele B., Hu Y. et al.: Reflectance estimation of canopy nitrogen content in winter wheat using optimized hyperspectral spectral indices and partial least squares regression. - Eur. J. Agron. 52: 198-209, 2014. Go to original source...
    24. Lichtenthaler H.K., Wellburn A.R.: Determinations of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. - Biochem. Soc. T. 11: 591-592, 1983. Go to original source...
    25. Lin F.F., Qiu L.F., Deng J.S. et al.: Investigation of SPAD meter-based indices for estimating rice nitrogen status. - Comput. Electron. Agr. 71: S60-S65, 2010. Go to original source...
    26. Ling Q., Huang W., Jarvis P.: Use of SPAD-502 meter to measure leaf chlorophyll concentration in Arabidopsis thaliana. - Photosynth. Res. 107: 209-214, 2011. Go to original source...
    27. Liu Z.A., Yang J.P., Yang Z.C.: Using a chlorophyll meter to estimate tea leaf chlorophyll and nitrogen contents. - J. Soil Sci. Plant Nut. 12: 339-348, 2012. Go to original source...
    28. Liu M., Liu X., Li M. et al.: Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. - Biosyst. Eng. 106: 223-233, 2010. Go to original source...
    29. Markwell J., Octerman J.C., Mitchell J.L.: Calibration of the Minolta SPAD-502 leaf chlorophyll meter. - Photosynth. Res. 46: 467-472, 1995. Go to original source...
    30. Moonrungsee N., Pencharee S., Jakmunee J.: Colorimetric analyzer based on mobile phone camera for determination of available phosphorus in soil. - Talanta 136: 204-209, 2015. Go to original source...
    31. Munoz-Huerta R.F., Guevara-Gonzalez R.G., Contreras-Medina L.M. et al.: A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. - Sensors 13: 10823-10843, 2013. Go to original source...
    32. Novichonok E.V., Novichonok A.O., Kurbatova J.A., Markovskaya, E.F.: Use of atLEAF+ chlorophyll meter for a nondestructive estimate of chlorophyll content. - Photosynthetica 54: 130-137, 2016. Go to original source...
    33. Osama K., Mishra B.N., Somvanshi P.: Machine learning technique in plant biology. - In: Barh, D., Khan M.S., Davies, E. (ed.): Plant Omics: The Omics of Plant Science. Pp. 731-754. Springer, India 2015. Go to original source...
    34. Pagola M., Ortiz R., Irigoyen I. et al.: New method to assess barley nitrogen nutrition status based on image colour analysis comparison with SPAD-502. - Comput. Electron. Agr. 65: 213-218, 2009. Go to original source...
    35. Photo Blog Stop. Accessed on August 24, 2017. Retrieved from http://photoblogstop.com/photoshop/accurate-white-balance-adjustments-in-photoshop, 2016.
    36. Pongnumkul S., Chaovalit P., Surasvadi N.: Applications of smartphone-based sensors in agriculture: a systematic review of research. - J. Sensors 15: 18-26, 2015. Go to original source...
    37. Pound M.P., French A.P.: An introduction to images and image analysis. - In: Dutta Gupta S., Ibaraki Y. (ed.): Plant Image Analysis: Fundamentals and Applications. Pp. 1-24. CRC Press, New York 2014. Go to original source...
    38. Prasad V.S.S., Dutta Gupta, S.: Applications and potentials of artificial neural networks in plant tissue culture. - In: Dutta Gupta S., Ibaraki Y. (ed.): Plant Tissue Culture Engineering. Pp. 47-67. Springer, Berlin 2008. Go to original source...
    39. RapidTables. Accessed on August 28, 2017. Retrieved from http://www.rapidtables.com/web/color/RGB_Color.htm, 2016.
    40. Ravier C., Quemada M., Jeuffroy M.-H.: Use of a chlorophyll meter to assess nitrogen nutrition index during the growth cycle in winter wheat. - Field Crop. Res. 214: 73-82, 2017. Go to original source...
    41. Reyniers M., Walvoort D.J.J., Baardemaaker J.D.: A linear model to predict with a multi-spectral radiometer the amount of nitrogen in winter wheat. - Int. J. Remote Sens. 27: 4159-4179, 2006. Go to original source...
    42. Riccardi M., Mele G., Pulvento C. et al.: Non-destructive evaluation of chlorophyll content in quinoa and amaranth by simple and multiple regression analysis of RGB image components. - Photosynth. Res. 120: 263-272, 2014. Go to original source...
    43. Rigon J.P.G., Capuani S., Beltrao N.E.M. et al.: Non-destructive determination of photosynthetic pigments in the leaves of castor oil plants. - Acta Sci.-Agron. 34: 325-329, 2012.
    44. Rigon J.P.G., Capuani S., Fernandes D.M., Guimarães, T.M.: A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. - Photosynthetica 54: 559-566, 2016. Go to original source...
    45. Rorie R.L., Purcell L.C., Karcher D.E., King, C.A.: The assessment of leaf nitrogen in corn from digital images. - Crop Sci. 51: 2174-2180, 2011a. Go to original source...
    46. Rorie R.L., Purcell L.C., Mozaffari M. et al.: Association of ʻgreennessʼ in corn with yield and leaf nitrogen concentration. - Agron. J. 103: 529-535, 2011b. Go to original source...
    47. Santos do Amaral E., Vieira Silva D., Dos Anjos L. et al. : Relationships between reflectance and absorbance chlorophyll indices with RGB (Red, Green, Blue) image components in seedlings of tropical tree species at nursery stage. - New Forest.: doi: 10.1007/s11056-018-9662-4, 2018. Go to original source...
    48. Sim C.C., Zaharah A.R., Tan M.S., Goh, K.J.: Rapid determination of leaf chlorophyll concentration, photosynthetic activity and NK concentration of Elaies guineensis via correlated SPAD-502 chlorophyll index. - Asian J. Agr. Res. 9: 132-138, 2015.
    49. Steele M.R., Gitelson A.A., Rundquist D.C.: A comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves. - Agron. J. 100: 779-782, 2008. Go to original source...
    50. Stefan M., Munteanu N., Stoleru V., Mihasan, M.: Effects of inoculation with plant growth promoting rhizobacteria on photosynthesis, antioxidant status and yield of runner bean. - Rom. Biotech. Lett. 18: 8132-8143, 2013.
    51. Uddling J., Gelang-Alfredsson J., Piikii K., Pleijel, H.: Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. - Photosynth. Res. 91: 37-46, 2007. Go to original source...
    52. Vesali F., Omid M., Kaleita A., Mobli, H.: Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. - Comput. Electron. Agr. 116: 211-220, 2015. Go to original source...
    53. Vesali F., Omid M., Mobli H., Kaleita A.: Feasibility of using smart phones to estimate chlorophyll content in corn plants. - Photosynthetica 55: 603-610, 2017. Go to original source...
    54. Vollmann J., Walter H., Sato T., Schweiger, P.: Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean. - Comput. Electron. Agr. 75: 190-195, 2011. Go to original source...
    55. Wang Y., Wang D., Zhang G., Wang, J.: Estimating nitrogen status of rice using the image segmentation of G-R thresholding method. - Field Crop. Res. 149: 33-39, 2013. Go to original source...
    56. Wang Y., Wang D., Shi P., Omasa, K.: Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still colour camera under natural light. - Plant Methods 10: 36, doi: 10.1186/1746-4811-10-36, 2014. Go to original source...
    57. Willmott C.J., Matsuura K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. - Clim. Res. 30: 79-82, 2005. Go to original source...
    58. Yadav S.P., Ibaraki Y., Dutta Gupta S.: Estimation of the chlorophyll content of micropropagated potato using RGB based image analysis. - Plant Cell Tiss. Org. 100: 183-188, 2010. Go to original source...
    59. Yuan Z., Ata-Ul-Karim S.T., Cao Q. et al.: Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readings. - Field Crop. Res. 185: 12-20, 2016. Go to original source...
    60. Živković Ž., Mihajlović I., Nikolić D.: Artificial neural network method applied on the nonlinear multivariate problems. - Serb. J. Manag. 4: 143-155, 2009.

    Return to the content