[go: up one dir, main page]

Photosynthetica 2017, 55(4):603-610 | DOI: 10.1007/s11099-016-0677-9

Feasibility of using smart phones to estimate chlorophyll content in corn plants

F. Vesali1, M. Omid1,*, H. Mobli1, A. Kaleita2
1 Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
2 Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, USA

New spectral absorption photometry methods are introduced to estimate chlorophyll (Chl) content of corn leaves by smart phones. The first method acquires light passing through a leaf by smartphone camera, compensating for differences in illumination conditions. In order to improve performance of the method, spectral absorption photometry (SAP) with background illumination has been considered as well. Data were acquired by smartphone camera in Iowa State University maize fields. Various indices were extracted and their correlation with Chl content were examined by Minolta SPAD-502. Hue index in SAP reached R 2 value of 0.59. However, with light-aided SAP (LASAP), R 2 of 0.97 was obtained. Among traits, the vegetation index gave the most accurate indication. We can conclude that the high performance of LASAP method for estimating Chl content, leads to new opportunities offered by smart phones at much lower cost. This is a highly accurate alternative to SPAD meters for estimating Chl content nondestructively.

Additional key words: Android phone; color index; SPAD meter; spectral absorption photometry

Received: April 3, 2016; Accepted: September 20, 2016; Published: December 1, 2017  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Vesali, F., Omid, M., Mobli, H., & Kaleita, A. (2017). Feasibility of using smart phones to estimate chlorophyll content in corn plants. Photosynthetica, 55(4), 603-610. doi: 10.1007/s11099-016-0677-9
Share...
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

    References

    1. de Souza E.G., Scharf P.C., Sudduth K.A.: Sun position and cloud effects on reflectance and vegetation indices of corn. - Agron J. 102: 734, 2010. Go to original source...
    2. Demotes-Mainard S., Boumaza R., Meyer S., Cerovic Z.G.: Indicators of nitrogen status for ornamental woody plants based on optical measurements of leaf epidermal polyphenol and chlorophyll contents. - Sci Hortic.-Amsterdam 115: 377-385, 2008. Go to original source...
    3. 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...
    4. Evans J.R.: Photosynthesis and nitrogen relationships in leaves of C3 plants-Oecologia 78: 9-19, 1989. Go to original source...
    5. Karcher D.E., Richardson M.D.: Quantifying turfgrass color using digital image analysis. - Crop Sci. 43: 943-951, 2003. Go to original source...
    6. 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...
    7. Li Y., Chen D., Walker C.N., Angus J.F.: Estimating the nitrogen status of crops using a digital camera. - Field Crop. Res. 118: 221-227, 2010. Go to original source...
    8. Lights L.A.: 334-15/T1c1-4wya led Datasheet. Pp. 1-10. Everlight Electronics Co., Ltd., New Taipei 2007.
    9. 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...
    10. Markwell J., Osterman J., Mitchell J.: Calibration of the Minolta SPAD-502 leaf chlorophyll meter. - Photosynth. Res. 46: 467-472, 1995. Go to original source...
    11. Miao Y., Mulla D.J., Randall G.W. et al.: Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. - Precis. Agric. 10: 45-62, 2009. Go to original source...
    12. Moonrungsee N., Pencharee S., Peamaroon N.: Determination of iron in zeolite catalysts by a smartphone camera-based colorimetric analyzer. - Instrum. Sci. Technol. 44: 401-409, 2016. Go to original source...
    13. Muñoz-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...
    14. Pongnumkul S., Chaovalit P., Surasvadi N.: Applications of smartphone-based sensors in agriculture: a systematic review of research. - J. Sensor. 15: 18-26, 2015. Go to original source...
    15. Porra R.J., Grimme L.H.: A new procedure for the determination of chlorophylls and band its application to normal and regreening chlorella. - Anal. Biochem. 57: 255-267, 1974. Go to original source...
    16. 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...
    17. 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...
    18. Rorie R.L., Purcell L.C., Mozaffari M. et al.: Association of "greenness" in corn with yield and leaf nitrogen concentration. - Agron. J. 103: 529-535, 2011b. Go to original source...
    19. Tewari V.K., Aruda A.K., Kumar S.P. et al.: Estimation of plant nitrogen content using digital image processing. - Agric. Eng. Int. CIGR J. 15: 78-86, 2013.
    20. Turner R.E., Rabalais N.N.: Changes in Mississippi river water quality this century. - BioScience 41: 140-147, 1991. Go to original source...
    21. 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...
    22. Wang Y., Wang D., Shi P., Omasa K.: Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. - Plant Methods 10: 1-11, 2014. Go to original source...
    23. 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...
    24. Willmott C.J., Ackleson S.G., Davis R.E. et al.: Statistics for the evaluation and comparison of models. - J. Geophys. Res.- Oceans 90: 8995-9005, 1985. Go to original source...
    25. Yadav S., Ibaraki Y., Dutta Gupta S.: Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. - Plant Cell Tiss. Organ Cult. 100: 183-188, 2010. Go to original source...

    Return to the content