Estimating SPAD, Nitrogen Concentration, and Chlorophyll Content in Rice Leaves using Calibrated Smartphone Digital Image

Authors

  • Valensi Kautsar Agrotechnology Department, Faculty of Agriculture, Institut Pertanian STIPER, Yogyakarta https://orcid.org/0000-0003-4748-7146
  • Kuni Faizah Faculty of Agricultural Engineering, Institut Pertanian STIPER, Yogyakarta
  • Arief Ika Uktoro Faculty of Agricultural Engineering, Institut Pertanian STIPER, Yogyakarta
  • Lutfiatun Khasanah
  • Filiphus Filiphus

DOI:

https://doi.org/10.18196/pt.v12i2.20553

Keywords:

Calibrated image, CIELAB, Color image processing, Leaf color, Nitrogen estimation

Abstract

Laboratory analysis is commonly used to determine nitrogen and chlorophyll content. However, smartphones can serve as rapid, mobile, and non-destructive tools for this purpose. An equation can be created to calculate nitrogen and chlorophyll content by analyzing color parameters from digital images of rice leaves. An examination was performed on 86 rice leaf samples from the maximum tillering and mature stages. Rice leaf photos were taken with a smartphone in natural outdoor lighting. Color calibration with Spydercheckr was needed to adjust for lighting conditions. Uncalibrated and calibrated image data were analyzed to determine RGB values converted into CIELAB color space. The L*, a*, and b* values had a significant correlation with SPAD parameters, nitrogen concentration, chlorophyll a, b, and total chlorophyll content. This connection was higher after image calibration. The study found that smartphone images could predict SPAD values with 87.9% to 92.3% precision, depending on color space. Using a smartphone digital picture of L* and a* values, N content could be estimated with 84.7% and 81.9% accuracy. Average accuracy for chlorophyll a, b, and total chlorophyll content was 65% to 76%. This study shows smartphone images can estimate rice leaf SPAD and nitrogen content. 

References

Astika, I. W., & Khayati, N. N. (2019, June). Prediction of color level and chlorophyll content of corn (Zea mays L.) leaves by using mobile phone cameras. IOP Conference Series: Materials Science and Engineering, 557(1), 012029. https://doi.org/10.1088/1757-899X/557/1/012029

Bartolome, G. J. C., de Mesa, J. P. S., Adoña, J. A. C., & Al Eugene, L. T. (2020). Performance of dye-sensitized solar cells with natural dye from local tropical plants. Mindanao Journal of Science and Technology, 18(1).

Caballero, D., Calvini, R., & Amigo, J. M. (2019). Hyperspectral imaging in crop fields: precision agriculture. Data Handling in Science and Technology, 32,453-473. https://doi.org/10.1016/B978-0-444-63977-6.00018-3

Cruz, G. D. (2019). Nitrogen deficiency mobile application for rice plant through image processing techniques. International Journal of Engineering and Advanced Technology, 8(6), 2950-2955. https://doi.org/10.35940/ijeat.F8721.088619

Evans, J. R., & Clarke, V. C. (2019). The nitrogen cost of photosynthesis. Journal of Experimental Botany, 70(1), 7-15. https://doi.org/10.1093/jxb/ery366

Gabriel, J., Quemada, M., Alonso-Ayuso, M., Lizaso, J., & Martín-Lammerding, D. (2019). Predicting N Status in Maize with Clip Sensors: Choosing Sensor, Leaf Sampling Point, and Timing. Sensors, 19(18), 3881. https://doi.org/10.3390/s19183881.

Ibrahim, N. U. A., Abd Aziz, S., Jamaludin, D., & Harith, H. H. (2021). Development of smartphone-based imaging techniques for the estimation of chlorophyll content in lettuce leaves. Food Research, 5(1), 33-38. https://doi.org/10.26656/fr.2017.5(S1).036

Mu, X., & Chen, Y. (2020). The physiological response of photosynthesis to nitrogen deficiency. Plant physiology and biochemistry: PPB, 158, 76-82. https://doi.org/10.1016/j.plaphy.2020.11.019.

Schneider, C. A., Rasband, W.S., & Eliceiri, K.W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https://doi.org/10.1038/nmeth.2089

Shrivastava, V. K., & Pradhan, M. K. (2021). Rice plant disease classification using color features: a machine learning paradigm. Journal of Plant Pathology, 103, 17-26. https://doi.org/10.1007/s42161-020-00683-3

Silalahi, N. H., Yudha, R. O., Dwiyanti, E. I., Zulvianita, D., Feranti, S. N., & Yustiana, Y. (2019). Government policy statements related to rice problems in Indonesia. Journal of Biological Science, Technology, and Management, 1(1), 35-41

Souza, W. S., de Oliveira, M. A., de Oliveira, G. M., de Santana, D. P., & de Araujo, R. E. (2018). Self-referencing method for relative color intensity analysis using mobile-phone. Optics and Photonics Journal, 8(07), 264. https://doi.org/10.4236/opj.2018.87022

Stirbet, A., Lazár, D., Guo, Y., & Govindjee, G. (2020). Photosynthesis: basics, history and modelling. Annals of Botany, 126(4), 511-537. https://doi.org/10.1093/aob/mcz171

Subedi, P., Sah, S. K., Marahattha, S., Panta, S., & Shrestha, J. (2018). Nitrogen use efficiency in dry direct seeded rice under LCC based nitrogen management. ORYZA-An International Journal on Rice, 55(4), 590-595. https://doi.org/10.5958/2249-5266.2018.00069.3

Sulistyorini, S., & Sunaryanto, L. T. (2020). Dampak Efisiensi Usahatani Padi Terhadap Peningkatan Produktvitas. Jambura Agribusiness Journal, 1(2), 43-51. https://doi.org/10.37046/jaj.v1i2.2680

Sunoj, S., Igathinathane, C., Saliendra, N., Hendrickson, J., & Archer, D. (2018). Color calibration of digital images for agriculture and other applications. ISPRS journal of photogrammetry and remote sensing, 146, 221-234. https://doi.org/10.1016/j.isprsjprs.2018.09.015

Wang, L., Chen, S., Li, D., Wang, C., Jiang, H., Zheng, Q., & Peng, Z. (2021). Estimation of paddy rice nitrogen content and accumulation both at leaf and plant levels from UAV hyperspectral imagery. Remote Sensing, 13(15), 2956. https://doi.org/10.3390/rs13152956

Wang, Y. P., Chang, Y. C., & Shen, Y. (2022). Estimation of nitrogen status of paddy rice at vegetative phase using unmanned aerial vehicle based multispectral imagery. Precision Agriculture, 23(1), 1-17. https://doi.org/10.1007/s11119-021-09823-w

Wu, Y., Al-Jumaili, S. J., Al-Jumeily, D., & Bian, H. (2022). Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression. Sensors, 22(22), 8626. https://doi.org/10.3390/s22228626

Zepka, L. Q., Jacob-Lopes, E., & Roca, M. (2019). Catabolism and bioactive properties of chlorophylls. Current Opinion in Food Science, 26, 94-100. https://doi.org/10.1016/j.cofs.2019.04.004

Zhang, K., Liu, X., Ma, Y., Zhang, R., Cao, Q., Zhu, Y., Cao, W., & Tian, Y. (2019). A Comparative Assessment of Measures of Leaf Nitrogen in Rice Using Two Leaf-Clip Meters. Sensors, 20(1),175 https://doi.org/10.3390/s20010175.

Downloads

Published

2024-08-29

Issue

Vol. 12 No. 2 (2024)

Section

Articles

License

COPYRIGHT

PLANTA TROPIKA is committed to its authors to protect and defend their work and their reputation and takes allegations of infringement, plagiarism, ethical disputes, and fraud very seriously. PLANTA TROPIKA is published under the terms of the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Authors retain copyright and grant the journal right of first publication (online and print) with the work simultaneously.

LICENSE

1. License to Publish
The non-commercial use of the article will be governed by the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The author hereby grants PLANTA TROPIKA an exclusive publishing and distribution license in the manuscript include tables, illustrations or other material submitted for publication as part of the manuscript (the “Article”) in print, electronic and all other media (whether now known or later developed), in any form, in all languages, throughout the world, for the full term of copyright, and the right to license others to do the same, effective when the article is accepted for publication. This license includes the right to enforce the rights granted hereunder against third parties.

2. Author’s Warranties
The author warrants that the article is original, written by stated author/s, has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).

3. User Rights
Under the Creative Commons Attribution-Non Commercial 4.0 International (CC BY-NC 4.0) license, the author(s) and users are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material). Users must give appropriate credit, provide a link to the license, and indicate if changes were made.

4. Rights of Authors
 Authors retain the following rights:

  • Copyright, and other proprietary rights relating to the article, such as patent rights,
  • The right to use the substance of the article in future own works, including lectures and books,
  • The right to reproduce the article for own purposes, provided the copies are not offered for sale, and
  • The right to self-archive the article.

5. Co-Authorship
If the article was prepared jointly with other authors, the signatory of this form warrants that he/she has been authorized by all co-authors to sign this agreement on their behalf, and agrees to inform his/her co-authors of the terms of this agreement.

6. Royalties
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by PLANTA TROPIKA or its sublicensee.

7. Miscellaneous
PLANTA TROPIKA will publish the article (or have it published) in the Journal if the article’s editorial process is successfully completed and PLANTA TROPIKA or its sublicensee has become obligated to have the article published. PLANTA TROPIKA may conform the article to a style of punctuation, spelling, capitalization, and usage that it deems appropriate.