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Current IssueEditorial BoardOnline First ArticlesArchive

Research Article

volume 35 issue 1-2 (march-june 2020) : 67-70,   Doi: 10.18805/BKAP213

Plant biomass estimation using image analysis and machine learning technique

T
Tanuj Mishra
A
Alka Arora
S
Sudeep Marwaha
M
Mrinmoy Ray
R
R. S. Tomar
1<div style="text-align: justify;">ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India</div>

  • Submitted03-07-2020|

  • Accepted18-07-2020|

  • First Online 10-09-2020|

  • doi 10.18805/BKAP213

Cite article:- Mishra Tanuj, Arora Alka, Marwaha Sudeep, Ray Mrinmoy, Tomar S. R. (2020). Plant biomass estimation using image analysis and machine learning technique. Bhartiya Krishi Anusandhan Patrika. 35(1): 67-70. doi: 10.18805/BKAP213.

ABSTRACT

Plant biomass is the basis for the calculation of net primary production. Estimation of fresh biomass in high throughput way is critical for plant phenotyping. Conventional phenotyping approaches for measuring the fresh biomass is time consuming, laborious and destructive in nature. Image analysis based plant phenotyping is very popular nowadays. Most of the approaches used projected shoot area from visual images (VIS) to estimate the fresh biomass. As water content has a significant effect on fresh biomass and water absorbs radiation at near infra-red (NIR) region (900nm to 1700nm), we have hypothesized that the combined use of VIS and NIR imaging can predict the fresh biomass more accurately that the VIS image alone. In this study, VIS and NIR images were collected using LemaTec facility installed at Nanaji Deshmukh Plant Phenomics Center, ICAR-IARI, New Delhi-12. In this study, VIS and NIR imaging were captured for rice leaves with different moisture content as a test case. MATLAB software (version 2015b) was used for image analysis. The two image derived parameter viz. Green Leaf Proportion (GPR) from VIS image and mean gray value/intensity (MGV_NIR) from NIR image were used to develop the statistical model to  estimate the fresh biomass in the form of Leaf Fresh Weight (LFW). The proposed approach significantly enhanced the fresh biomass estimation.

REFERENCES

  1. Bruinsma J. (2003). World Agriculture Towards 2015/    2030"an FAO perspective. Earthscan, London.
  2. Ebrahimi M, Sinegani A A S, Sarikhani M R and Mohammadi S A. (2017). Comparison of 
  3. artificial neural network and multivariate regression models for prediction of Azotobacteria population in soil under different land uses. Computers and Electronics in Agriculture 140: 409-421.
  4. Fernández R, Montes H and Salinas C. (2015). VIS-    NIR, SWIR and LWIR imagery for estimation of ground bearing capacity. Sensors 15(6): 13994-14015.
  5. Furbank R T, von Caemmerer S, Sheehy J and Edwards G. (2009). C4 rice: a challenge for plant phenomics. Functional Plant Biology 36(11):845-856.
  6. Gehan M A, Fahlgren N, Abbasi A, Berry J C, Callen S T, Chavez L and Hoyer J S. (2017). Plant CV v2: Image analysis software for high-throughput plant phenotyping. PeerJ, 5, e4088.
  7. Golzarian M R, Frick R A, Rajendran K, Berger B, Roy S, Tester M and Lun DS. (2011). Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7: 1–11.
  8. Jansen M, Gilmer F, Biskup B, Nagel K A, Rascher U, Fischbach A, ... and De Jaeger I. (2009). Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Functional Plant Biology 36(11): 902-914.
  9. Lillesand T, Kiefer R W and Chipman J. (2015). Remote sensing and image interpretation. 
  10. John Wiley & Sons.
  11. Mizoue N and Masutani T. (2003). Image analysis measure of crown condition, foliage biomass 
  12. and stem growth relationships of Chamaecyparis obtusa. Forest Ecology and Management    172(1): 79-88
  13. Niklas K J and Enquist B J. (2002). On the vegetative biomass partitioning of seed plant leaves, stems, and roots. The American Naturalist    159(5): 482-497.
  14. Otsu N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on 
  15. systems, man and cybernetics 9(1): 62-66.
  16. Paruelo J M, Lauenroth W K and Roset P A. (2000). Estimating aboveground plant biomass using a photographic technique. Journal of Range Management Archives 53(2): 190-193.
  17. Poorter H and Nagel O. 2000. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Functional Plant Biology 27(12): 1191-1191.
  18. Schirrmann M, Hamdorf A, Garz A, Ustyuzhanin A and Dammer K H. (2016). Estimating wheat biomass by combining image clustering with crop height. Computers and Electronics in Agriculture 121: 374-384.
  19. Seelig H D, Hoehn A, Stodieck L S, Klaus D M, Adams Iii W W, and Emery W J. (2008). The assessment of leaf water content using leaf reflectance ratios in the visible, near, and shortwaveinfrared. International Journal of Remote Sensing 29(13): 3701-3713.
  20. Stenberg B, Rossel RAV, Mouazen A M and Wetterlind J. (2010). Visible and near infrared ectroscopy in soil science. In Advances in agronomy 107: 163-215. Academic Press.
  21. Sticklen M B. (2007). Feedstock crop genetic engineering for alcohol fuels. Crop science 47(6): 2238-2248.
  22. Tsaftaris S A, Minervini M and Scharr H. (2016). Machine learning for plant phenotyping needs image processing. Trends in plant science 21(12): 989-991. 
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    Research Article

    volume 35 issue 1-2 (march-june 2020) : 67-70,   Doi: 10.18805/BKAP213

    Plant biomass estimation using image analysis and machine learning technique

    T
    Tanuj Mishra
    A
    Alka Arora
    S
    Sudeep Marwaha
    M
    Mrinmoy Ray
    R
    R. S. Tomar
    1<div style="text-align: justify;">ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India</div>

    • Submitted03-07-2020|

    • Accepted18-07-2020|

    • First Online 10-09-2020|

    • doi 10.18805/BKAP213

    Cite article:- Mishra Tanuj, Arora Alka, Marwaha Sudeep, Ray Mrinmoy, Tomar S. R. (2020). Plant biomass estimation using image analysis and machine learning technique. Bhartiya Krishi Anusandhan Patrika. 35(1): 67-70. doi: 10.18805/BKAP213.

    ABSTRACT

    Plant biomass is the basis for the calculation of net primary production. Estimation of fresh biomass in high throughput way is critical for plant phenotyping. Conventional phenotyping approaches for measuring the fresh biomass is time consuming, laborious and destructive in nature. Image analysis based plant phenotyping is very popular nowadays. Most of the approaches used projected shoot area from visual images (VIS) to estimate the fresh biomass. As water content has a significant effect on fresh biomass and water absorbs radiation at near infra-red (NIR) region (900nm to 1700nm), we have hypothesized that the combined use of VIS and NIR imaging can predict the fresh biomass more accurately that the VIS image alone. In this study, VIS and NIR images were collected using LemaTec facility installed at Nanaji Deshmukh Plant Phenomics Center, ICAR-IARI, New Delhi-12. In this study, VIS and NIR imaging were captured for rice leaves with different moisture content as a test case. MATLAB software (version 2015b) was used for image analysis. The two image derived parameter viz. Green Leaf Proportion (GPR) from VIS image and mean gray value/intensity (MGV_NIR) from NIR image were used to develop the statistical model to  estimate the fresh biomass in the form of Leaf Fresh Weight (LFW). The proposed approach significantly enhanced the fresh biomass estimation.

    REFERENCES

    1. Bruinsma J. (2003). World Agriculture Towards 2015/    2030"an FAO perspective. Earthscan, London.
    2. Ebrahimi M, Sinegani A A S, Sarikhani M R and Mohammadi S A. (2017). Comparison of 
    3. artificial neural network and multivariate regression models for prediction of Azotobacteria population in soil under different land uses. Computers and Electronics in Agriculture 140: 409-421.
    4. Fernández R, Montes H and Salinas C. (2015). VIS-    NIR, SWIR and LWIR imagery for estimation of ground bearing capacity. Sensors 15(6): 13994-14015.
    5. Furbank R T, von Caemmerer S, Sheehy J and Edwards G. (2009). C4 rice: a challenge for plant phenomics. Functional Plant Biology 36(11):845-856.
    6. Gehan M A, Fahlgren N, Abbasi A, Berry J C, Callen S T, Chavez L and Hoyer J S. (2017). Plant CV v2: Image analysis software for high-throughput plant phenotyping. PeerJ, 5, e4088.
    7. Golzarian M R, Frick R A, Rajendran K, Berger B, Roy S, Tester M and Lun DS. (2011). Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7: 1–11.
    8. Jansen M, Gilmer F, Biskup B, Nagel K A, Rascher U, Fischbach A, ... and De Jaeger I. (2009). Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Functional Plant Biology 36(11): 902-914.
    9. Lillesand T, Kiefer R W and Chipman J. (2015). Remote sensing and image interpretation. 
    10. John Wiley & Sons.
    11. Mizoue N and Masutani T. (2003). Image analysis measure of crown condition, foliage biomass 
    12. and stem growth relationships of Chamaecyparis obtusa. Forest Ecology and Management    172(1): 79-88
    13. Niklas K J and Enquist B J. (2002). On the vegetative biomass partitioning of seed plant leaves, stems, and roots. The American Naturalist    159(5): 482-497.
    14. Otsu N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on 
    15. systems, man and cybernetics 9(1): 62-66.
    16. Paruelo J M, Lauenroth W K and Roset P A. (2000). Estimating aboveground plant biomass using a photographic technique. Journal of Range Management Archives 53(2): 190-193.
    17. Poorter H and Nagel O. 2000. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Functional Plant Biology 27(12): 1191-1191.
    18. Schirrmann M, Hamdorf A, Garz A, Ustyuzhanin A and Dammer K H. (2016). Estimating wheat biomass by combining image clustering with crop height. Computers and Electronics in Agriculture 121: 374-384.
    19. Seelig H D, Hoehn A, Stodieck L S, Klaus D M, Adams Iii W W, and Emery W J. (2008). The assessment of leaf water content using leaf reflectance ratios in the visible, near, and shortwaveinfrared. International Journal of Remote Sensing 29(13): 3701-3713.
    20. Stenberg B, Rossel RAV, Mouazen A M and Wetterlind J. (2010). Visible and near infrared ectroscopy in soil science. In Advances in agronomy 107: 163-215. Academic Press.
    21. Sticklen M B. (2007). Feedstock crop genetic engineering for alcohol fuels. Crop science 47(6): 2238-2248.
    22. Tsaftaris S A, Minervini M and Scharr H. (2016). Machine learning for plant phenotyping needs image processing. Trends in plant science 21(12): 989-991. 
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