Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration

Ronnie Concepcion II, Elmer Dadios, Argel Bandala, Joel Cuello, Yutaka Kodama

Abstract


Optimizing photosynthesis is vital in maintaining quality farm produce in the agricultural food production sector. The nonlinear behavior of the interaction of crop pre-harvest growth factors can promote or retard its growth. This study employed multigene symbolic regression genetic programming (MSRGP) in developing the chlorophyll-a fitness function allied with bioinspired algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and multiverse optimization (MVO), in determining the ideal combination of carbon dioxide, light intensity, air temperature, and humidity that will induce photosynthesis based on aquaponic lettuce (Lactuca sativa var. Altima) leaf chlorophyll-a concentration. Light spectra were characterized through the floating leaf disk technique, which resulted in white spectra as the most photosynthetic conducive based on the light reaction and dark respiration. Leaf spectro-textural-morphological signatures were extracted for non-destructive MSRGP chlorophyll-a concentration measurement. Carbon dioxide and humidity have a strong positive impact on chlorophyll-a concentration. Photosynthesis is impeded by Vis/IR above 7.817. The hybrid MSRGP-MVO generated the ideal global solution of 880.744 ppm of CO2, 543.147 μmol m-2 s-1 of the visible white light spectrum, 22.238 °C air temperature, and 67.742% humidity which resulted in 651.144 mg g-1 of Chl-a, 0.934 leaf weight ratio, 0.066 roots to shoot ratio, 141 xylem vessels mm-2 127.389 stomata mm-2, and more prominent intracellular chloroplast concentration for the harvest stage lettuce. The established standard for fresh weight, chlorophylls a and b, and vitamin C concentrations are essential for developing an adaptive nutrient management system to maintain the expected growth signatures of lettuce at the end of the 6-week cultivation cycle.


Keywords


Chlorophyll pigment; genetic programming; lettuce; multiverse optimization; photosynthetic rate.

Full Text:

PDF

References


I. Valenzuela, R. Baldovino, A. Bandala, and E. Dadios, “Optimization of Photosynthetic Rate Parameters Using Adaptive Neuro-Fuzzy Inference System (ANFIS),” in 2017 International Conference on Computer and Applications2, 2017, pp. 129–134.

I. Valenzuela, R. Baldovino, A. Bandala, and E. Dadios, “Pre-harvest factors optimization using genetic algorithm for lettuce,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 1–4, pp. 159–163, 2018.

L. Cammarisano, I. S. Donnison, and P. R. H. Robson, “Producing Enhanced Yield and Nutritional Pigmentation in Lollo Rosso Through Manipulating the Irradiance, Duration, and Periodicity of LEDs in the Visible Region of Light,” Front. Plant Sci., vol. 11, no. 598082, pp. 1–10, 2020.

C. Amitrano, Rouphael Youssef, S. De Pascale, and V. De Micco, “Modulating Vapor Pressure Deficit in the Plant Micro-Environment May Enhance the Bioactive Value of Lettuce,” Horticulturae, vol. 7, no. 32, pp. 1–15, 2021.

S. Lauguico, R. Baldovino, R. Concepcion, J. Alejandrino, R. R. Tobias, and E. Dadios, “Adaptive Neuro-Fuzzy Inference System on Aquaphotomics Development for Aquaponic Water Nutrient Assessments and Analyses,” in ICITEE 2020 - Proceedings of the 12th International Conference on Information Technology and Electrical Engineering, 2020, pp. 317–322.

R. Concepcion II and E. Dadios, “Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums,” AGRIVITA J. Agric. Sci., vol. 43, no. 1, pp. 174–189, 2021.

N. Kalhori, T. Ying, R. Nulit, M. Sahebi, R. Abiri, and N. Atabaki, “Effect of Four Different Salts on Seed Germination and Morphological Characteristics of Oryza sativa L. cv. MR219,” Int. J. Adv. Res. Bot., vol. 4, no. 1, pp. 29–45, 2018.

N. Kasozi, R. Tandlich, M. Fick, H. Kaiser, and B. Wilhelmi, “Iron supplementation and management in aquaponic systems: A review,” Aquac. Reports, vol. 15, no. 100221, 2019.

R. G. de Luna, E. P. Dadios, A. A. Bandala, and R. R. P. Vicerra, “Size classification of tomato fruit using thresholding, machine learning and deep learning techniques,” Agrivita, vol. 41, no. 3, pp. 586–596, 2019.

C. F. Strock et al., “Seedling root architecture and its relationship with seed yield across diverse environments in Phaseolus vulgaris,” F. Crop. Res., vol. 237, no. April, pp. 53–64, 2019.

D. F. da Cruz, Eleandro Silva Medici, Leonardo Oliveira dos Santos Leles, Paulo Sergio Ambrozim, Clodoaldo Spadeto Souza, Wendell Luccas de Carvalho, “Growth of black pepper plantlets under different substrates and irrigation levels,” Sci. Agric., vol. 79, no. 1, pp. 1–6, 2022.

H. Spalholz and R. Hernandez, “Transplant lettuce response to different blue: red photon flux ratios in indoor LED sole-source lighting production,” Acta Hor, vol. 1227, no. 70, pp. 555–562, 2018.

J. A. Thomas, M. Vasiliev, M. Nur-e-alam, and K. Alameh, “Increasing the Yield of Lactuca sativa, L. in Glass Greenhouses through Illumination Spectral Filtering and Development of an Optical Thin Film Filter,” Sustainability, vol. 12, no. 3740, pp. 1–17, 2020.

C.-H. Kuo, Y.-C. Chou, K.-C. Liao, C.-J. Shieh, and T.-S. Deng, “Optimization of Light Intensity, Temperature, and Nutrients to Enhance the Bioactive Content of Hyperforin and Rutin in St. John’s Wort,” Molecules, vol. 25, no. 4256, pp. 1–16, 2020.

Z. Yi, J. Cui, Y. Fu, J. Yu, and H. Liu, “Optimization of light intensity and nitrogen concentration in solutions regulating yield, vitamin C, and nitrate content of lettuce,” J. Hortic. Sci. Biotechnol., pp. 1–10, 2020.

T. D. de Souza, A. C. Borges, A. T. de Matos, R. W. Veloso, and A. F. Braga, “Kinetics of Arsenic Absorption by the Species Eichhornia crassipes and Lemna valdiviana Under Optimized Conditions,” Chemosphere, vol. 209, pp. 866–874, 2018.

R. Concepcion II, S. Lauguico, J. Alejandrino, E. Dadios, and E. Sybingco, “Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrix,” AGRIVITA J. Agric. Sci., vol. 42, no. 3, pp. 472–486, 2020.

D. Jung, H. Kim, J. Y. Kim, T. Lee, and S. H. Park, “Design Optimization of Proportional Plus Derivative Band Parameters Used in Greenhouse Ventilation by Response Surface Methodology,” Hortic. Sci. Technol., vol. 38, no. 2, pp. 187–200, 2020.

Z. O. U. Jie, Z. Cheng-bo, X. Hong, C. Rui-feng, Y. Qi-chang, and L. Tao, “The effect of artificial solar spectrum on growth of cucumber and lettuce under controlled environment,” J. Integr. Agric., vol. 19, no. 8, pp. 2027–2034, 2020.

B. Kump, “The role of far-red light ( FR ) in photomorphogenesis and its use in greenhouse plant production,” Acta Agric. Slov., vol. 116, no. 1, pp. 93–105, 2020.

L. G. Izzo, M. A. Mickens, G. Aronne, and C. Gómez, “Spectral effects of blue and red light on growth, anatomy, and physiology of lettuce,” Physiol. Plant., pp. 1–12, 2021.

L. Marcos and K. V. Mai, “Light Spectra Optimization in Indoor Plant Growth for Internet of Things,” in 2020 IEEE International IOT, Electronics and Mechatronics Conference, 2020.

T. Zou, C. Huang, P. Wu, L. Ge, and Y. Xu, “Optimization of Artificial Light for Spinach Growth in Plant Factory Based on Orthogonal Test,” Plants, vol. 9, no. 40, pp. 1–14, 2020.

C. Urairi, H. Shimizu, H. Nakashima, J. Miyasaka, and K. Ohdoi, “Optimization of Light-Dark Cycles of Lactuca sativa L . in Plant Factory,” Environ. Control Biol., vol. 55, no. 2, pp. 85–91, 2017.

A. I. Eismann, R. P. Reis, A. Ferreira, and D. Negrão, “Ulva spp . carotenoids: Responses to environmental conditions,” Algal Res., vol. 48, no. 101916, pp. 1–18, 2020.

I. Alsiņa, M. Dūma, L. Dubova, A. Šenberga, and S. Daģis, “Comparison of different chlorophylls determination methods for leafy vegetables,” Agron. Res., vol. 14, no. 2, pp. 309–316, 2016.

E. G. Kulikova, S. Y. Efremova, N. Politaeva, and Y. Smyatskaya, “Efficiency of an alternative LED-based grow light system,” IOP Conf. Ser. Earth Environ. Sci., vol. 288, no. 012064, pp. 1–5, 2018.

M. Hikawa, K. Nishizawa, and Y. Kodama, “Prediction of prospective leaf morphology in lettuce based on intracellular chloroplast position,” Sci. Hortic. (Amsterdam)., vol. 251, pp. 20–24, 2019.

R. J. Lee, S. R. Bhandari, G. Lee, and Lee Jun Gu, “Optimization of temperature and light, and cultivar selection for the production of high ‑ quality head lettuce in a closed ‑ type plant factory,” Hortic. Environ. Biotechnol., vol. 60, no. 2, pp. 207–216, 2019.

D. Loconsole, G. Cocetta, P. Santoro, and A. Ferrante, “Optimization of LED Lighting and Quality Evaluation of Romaine Lettuce Grown in An Innovative Indoor Cultivation System,” Sustainability, vol. 11, no. 841, pp. 1–16, 2019.

G. M. Maciel, R. B. de Araujo Gallis, R. L. Barbosa, L. M. Pereira, A. C. S. Siquieroli, and J. V. M. Peixoto, “Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels,” Bragantia, vol. 79, no. 2, pp. 224–235, 2020.

G. Pennisi et al., “Optimal light intensity for sustainable water and energy use in indoor cultivation of lettuce and basil under red and blue LEDs,” Sci. Hortic. (Amsterdam)., vol. 272, no. 2019, pp. 1–10, 2020.

Y. C. F. Salsinha, Maryani, D. Indradewa, Y. A. Purwestri, and D. Rahmawati, “Morphological and anatomical characteristics of Indonesian rice roots from East Nusa Tenggara contribute to drought tolerance,” Asian J. Agric. Biol., no. 1, pp. 1–11, 2021.

S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, “Multiverse Optimizer: a nature-inspired algorithm for global optimization,” Neural Comput. Appl., vol. 27, no. 2, pp. 495–513, 2016.

M. G. B. Palconit et al., “Towards Tracking: Investigation of Genetic Algorithm and LSTM as Fish Trajectory Predictors in Turbid Water,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2020-November, no. 1, pp. 744–749, 2020.

V. J. D. Almero et al., “Genetic algorithm-based dark channel prior parameters selection for single underwater image dehazing,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2020-November, pp. 1153–1158, 2020.




DOI: http://dx.doi.org/10.18517/ijaseit.11.6.14991

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development