Non-destructive in Situ Measurement of Aquaponic Lettuce Leaf Photosynthetic Pigments and Nutrient Concentration Using Hybrid Genetic Programming

Ronnie S. Concepcion II, Elmer P. Dadios, Joel Cuello

Abstract


Phytopigment and nutrient concentration determination normally rely on laboratory chemical analysis. However, non-destructive and onsite measurements are necessary for intelligent closed environment agricultural systems. In this study, the impact of photosynthetic light treatments on aquaponic lettuce leaf canopy (Lactuca sativa var. Altima) was evaluated using UV-Vis spectrophotometry (300-800 nm), fourier transform infrared spectroscopy (4000-500 per cm), and the integrated computer vision and computational intelligence. Hybrid decision tree and multigene symbolic regression genetic programming (DT-MSRGP) exhibited the highest predictive accuracies of 80.9%, 89.9%, 83.5%, 85.5%, 81.3%, and 83.4% for chlorophylls a and b, β-carotene, anthocyanin, lutein, and vitamin C concentrations present in lettuce leaf canopy based on spectro-textural-morphological signatures. An increase in β-carotene and anthocyanin concentrations verified that these molecular pigments act as a natural sunscreen to protect lettuce from light stress and an increase in chlorophylls a and b ratio in the white light treatment corresponds to reduced emphasis on photon energy absorbance in chloroplast photosystem II. Red-blue light induces chlorophyll b concentration while white light promotes all other pigments and vitamin C. It was confirmed that the use of the DT-MSRGP model is essential as the concentration of phytopigment and nutrients significantly change during the head development and harvest stages.

Keywords


Computer vision; Leaf nutrient level prediction; Leaf pigment prediction; Lettuce; Machine learning

Full Text:

PDF

References


Alsiņa, I., Dūma, M., Dubova, L., Šenberga, A., & Daģis, S. (2016). Comparison of different chlorophylls determination methods for leafy vegetables. Agronomy Research, 14(2), 309–316. Retrieved from https://www.cabdirect.org/cabdirect/abstract/20163195243

Ben Ghnaya, A., Charles, G., Hourmant, A., Ben Hamida, J., & Branchard, M. (2007). Morphological and physiological characteristics of rapeseed plants regenerated in vitro from thin cell layers in the presence of zinc. Comptes Rendus - Biologies, 330(10), 728–734. https://doi.org/10.1016/j.crvi.2007.07.004

Concepcion II, R. S., & Dadios, E. P. (2021). Bioinspired optimization of germination nutrients based on Lactuca sativa seedling root traits as influenced by seed stratification, fortification and light spectrums. AGRIVITA Journal of Agricultural Science, 43(1), 174-189. https://doi.org/10.17503/agrivita.v43i1.2843

Concepcion, R. S., Lauguico, S. C., Tobias, R. R., Dadios, E. P., Bandala, A. A., & Sybingco, E. (2020). Estimation of photosynthetic growth signature at the canopy scale using new genetic algorithmmodified visible band triangular greenness index. In 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS). Taipei, Taiwan: IEEE. https://doi.org/10.1109/ARIS50834.2020.9205787

Gholami, M., Rahemi, M., Kholdebarin, B., & Rastegar, S. (2012). Biochemical responses in leaves of four fig cultivars subjected to water stress and recovery. Scientia Horticulturae, 148, 109–117. https://doi.org/10.1016/j.scienta.2012.09.005

Hägele, F., Baur, S., Menegat, A., Gerhards, R., Carle, R., & Schweiggert, R. M. (2016). Chlorophyll fluorescence imaging for monitoring the effects of minimal processing and warm water treatments on physiological properties and quality attributes of fresh-cut salads. Food and Bioprocess Technology, 9(4), 650–663. https://doi.org/10.1007/s11947-015-1661-2

Henderson, I. M. (2015). Ratios, proportions, and mixtures of chlorophylls: Corrections to spectrophotometric methods and an approach to diagnosis. Limnology and Oceanography: Methods, 13(11), 617–629. https://doi.org/10.1002/lom3.10052

Kapur, A., Haskovic, A., Copra-Janicijevic, A., Klepo, L., Topcagic, A., Tahirovic, A., & Sofic, E. (2012). Spectrophotometric analysis of total ascorbic acid contetnt in various fruits and vegetables. Bulletin of the Chemists and Technologists of Bosnia and Herzegovina, 38, 39–42. Retrieved from http://hemija.pmf.unsa.ba/glasnik/files/Issue%2038/38%20-%208-Kapur.pdf

Lauguico, S. C., Concepcion II, R. S., Alejandrino, J. D., Tobias, R. R., & Dadios, E. P. (2020). Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes. International Journal of Advances in Intelligent Informatics, 6(2), 173-184. https://doi.org/10.26555/ijain.v6i2.466

Lee, J., Durst, R. W., & Wrolstad, R. E. (2005). Determination of total monomeric anthocyanin pigment content of fruit juices, beverages, natural colorants, and wines by the pH differential method: Collaborative study. Journal of AOAC International, 88(5), 1269–1278. https://doi.org/10.1093/jaoac/88.5.1269

Li, Y., Scales, N., Blankenship, R. E., Willows, R. D., & Chen, M. (2012). Extinction coefficient for red-shifted chlorophylls: Chlorophyll d and chlorophyll f. Biochimica et Biophysica Acta, 1817(8), 1292–1298. https://doi.org/10.1016/j.bbabio.2012.02.026

Liu, H., Fu, Y., Wang, M., & Liu, H. (2016). Green light enhances growth, photosynthetic pigments and CO 2 assimilation efficiency of lettuce as revealed by ‘knock out’ of the 480–560 nm spectral waveband. Photosynthetica, 54, 144–152. https://doi.org/10.1007/s11099-016-0233-7

Loresco, P. J. M., & Dadios, E. P. (2018). A scale-invariant lettuce leaf area calculation using machine vision and knowledge-based methods. International Journal of Engineering & Technology, 7(4), 4880-4885. https://doi.org/ 10.14419/ijet.v7i4.26553

Odabas, M. S., Simsek, H., Lee, C. W., & İseri, İ. (2017). Multilayer perceptron neural network approach to estimate chlorophyll concentration index of lettuce (Lactuca sativa L.). Communications in Soil Science and Plant Analysis, 48(2), 162–169. https://doi.org/10.1080/00103624.2016.1253726

Olotu, P. N., Zechariah, J., Ibochi, J. I., Shaibu, K. B., Onche, E. U., Olotu, I. A., & Ajima, U. (2020). Vitamin C quantification and elemental analysis of five local vegetables in jos-south local government area of Plateau State, Nigeria. Journal of Natural Product and Plant Resources, 10(3), 1–5. Retrieved from https://www.scholarsresearchlibrary.com/articles/vitamincquantification-and-elemental-analysis-of-fivelocal-vegetables-in-jossouth-local-governmentarea-of-plateau-sta.pdf

Pacumbaba, R. O., & Beyl, C. A. (2011). Changes in hyperspectral reflectance signatures of lettuce leaves in response to macronutrient deficiencies. Advances in Space Research, 48(1), 32–42. https://doi.org/10.1016/j.asr.2011.02.020

Ritchie, R. J. (2006). Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynthesis Research, 89(1), 27–41. https://doi.org/10.1007/s11120-006-9065-9

Ritchie, R. J. (2008). Universal chlorophyll equations for estimating chlorophylls a, b, c, and d and total chlorophylls in natural assemblages of photosynthetic organisms using acetone, methanol, or ethanol solvents. Photosynthetica, 46(1), 115–126. https://doi.org/10.1007/s11099-008-0019-7

Santos, D. A., Lima, K. P., Março, P. H., & Valderrama, P. (2016). Vitamin C determination by ultraviolet spectroscopy and multiproduct calibration. Journal of the Brazilian Chemical Society, 27(10), 1912–1917. https://doi.org/10.5935/0103-5053.20160071

Shah, S., Buraidah, M. H., Teo, L. P., Careem, M. A., & Arof, A. K. (2016). Dye-sensitized solar cells with sequentially deposited anthocyanin and chlorophyll dye as sensitizers. Optical and Quantum Electronics, 48(3), 1–8. https://doi.org/10.1007/s11082-016-0492-3

Song, J., Huang, H., Hao, Y., Song, S., Zhang, Y., Su, W., & Liu, H. (2020). Nutritional quality, mineral and antioxidant content in lettuce affected by interaction of light intensity and nutrient solution concentration. Scientific Reports, 10(1), 1–9. https://doi.org/10.1038/s41598-020-59574-3

Steidle Neto, A. J., de Oliveira Moura, L., de Carvalho Lopes, D., de Almeida Carlos, L., Martins, L. M., & de Castro Louback Ferraz, L. (2016). Nondestructive prediction of pigment contents in lettuce based on Vis-NIR spectroscopy. Journal of the Science of Food and Agriculture, 97(7), 2015-2022. https://doi.org/10.1002/jsfa.8002

Sublett, W. L., Casey Barickman, T., & Sams, C. E. (2018). Effects of elevated temperature and potassium on biomass and quality of dark red ‘lollo rosso’ lettuce. Horticulturae, 4(2), 11. https://doi.org/10.3390/horticulturae4020011

Tarrago-Trani, M. T., Phillips, K. M., & Cotty, M. (2012). Matrix-specific method validation for quantitative analysis of vitamin C in diverse foods. Journal of Food Composition and Analysis, 26(1–2), 12–25. https://doi.org/10.1016/j.jfca.2012.03.004

Thrane, J. E., Kyle, M., Striebel, M., Haande, S., Grung, M., Rohrlack, T., & Andersen, T. (2015). Spectrophotometric analysis of pigments: A critical assessment of a high-throughput method for analysis of algal pigment mixtures by spectral deconvolution. PLoS ONE, 10(9), 1–24. https://doi.org/10.1371/journal.pone.0137645

Yang, X., Zhang, J., Guo, D., Xiong, X., Chang, L., Niu, Q., & Huang, D. (2016). Measuring and evaluating anthocyanin in lettuce leaf based on color information. IFAC-PapersOnLine, 49(16), 96–99. https://doi.org/10.1016/j.ifacol.2016.10.018

Zeng, C. (2013). Effects of different cooking methods on the vitamin C content of selected vegetables. Nutrition and Food Science, 43(5), 438–443. https://doi.org/10.1108/NFS-11-2012-0123




DOI: http://doi.org/10.17503/agrivita.v43i3.2961

Copyright (c) 2021 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.