Optimization of Aquaponic Lettuce Evapotranspiration Based on Artificial Photosynthetic Light Properties Using Hybrid Genetic Programming and Moth Flame Optimizer

Mary Grace Ann Bautista, Ronnie Concepcion II, Argel Bandala, Christan Hail Mendigoria, Elmer Dadios

Abstract


Land and water resources, climate change, and disaster risks significantly affect the agricultural sector. An effective solution for growing crops to improve productivity and optimize the use of resources is through controlled-environment agriculture (CEA). Evapotranspiration (ET) is an important greenhouse crop attribute that can be optimized for optimum plant growth. Light intensity and radiation are significant for controlling ET. To address this challenge, this study successfully determined the properties of optimum artificial light for minimum evapotranspiration rate of head development-stage and harvest-stage lettuce under light-period and dark-period using genetic programming and bio-inspired algorithms namely, grey wolf optimization (GWO), whale optimization algorithm (WOA), dragonfly algorithm (DA), and moth flame optimization (MFO). MFO provided the optimized global solution for the configured models. Results showed that head development-stage lettuce requires higher light intensity with lower visible to infrared radiation ratio (Vis/IR) than harvest-stage lettuce when exposed to light. On the other hand, harvest-stage lettuce requires higher light intensity with lower Vis/IR than head development-stage under dark-period respiration reaction. Findings of this study can be utilized in growing and improving yield crops in controlled-environment agriculture.

Keywords


Aquaponic lettuce; Artificial light properties bio-inspired optimization; Evapotranspiration rate; Genetic programming

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References


Bautista, M., Alejandrino, J., Alajas, O., Mendigoria, C., Concepcion R., Dadios, E., Bandala, A., & Vicerra, R. (2022). 8-10-Gene Expression-Based aquaponic lettuce evapotranspiration optimization based on photosynthetic light properties. Proceedings of the International Conference on Intelligent Computing & Optimization (ICO2022), 674 – 685. DOI

Benke, K., & Tomkins, B. (2017). Future food-production systems: Vertical farming and controlled-environment agriculture. Sustainability: Science, Practice and Policy, 13(1), 13–26. DOI

Cammarisano, L., Donnison, I. S., & Robson, P. R. H. (2019). Producing enhanced yield and nutritional pigmentation in Lollo Rosso through manipulating the irradiance, duration, and periodicity of LEDs in the visible region of light. Frontiers in Plant Science, 11, 2019. DOI

Camejo, D., Frutos, A., Mestre, T. C., del Carmen Piñero, M., Rivero, R. M., & Martínez, V. (2020). Artificial light impacts the physical and nutritional quality of lettuce plants. Horticulture Environment and Biotechnology, 61(1), 69–82. DOI

Chen, X. li, Wang, L. chun, Li, T., Yang, Q. chang, & Guo, W. zhong. (2019). Sugar accumulation and growth of lettuce exposed to different lighting modes of red and blue LED light. Scientific Reports, 9(1). DOI

Chen, X. li, Yang, Q. chang, Song, W. pin, Wang, L. chun, Guo, W. zhong, & Xue, X. zhang. (2017). Growth and nutritional properties of lettuce affected by different alternating intervals of red and blue LED irradiation. Scientia Horticulturae, 223, 44–52. DOI

Concepcion, R., Dadios, E., Bandala, A., Cuello, J., & Kodama, Y. (2021). Hybrid genetic programming and multiverse-based optimization of pre-harvest growth factors of aquaponic lettuce based on chlorophyll concentration. International Journal on Advanced Science, Engineering and Information Technology, 11(6), 2128–2138. DOI

Darwish, A. (2018). Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications. Future Computing and Informatics Journal, 3(2), 231–246. DOI

Esmaili, M., Aliniaeifard, S., Mashal, M., Vakilian, K. A., Ghorbanzadeh, P., Azadegan, B., Seif, M., & Didaran, F. (2021). Assessment of adaptive neuro-fuzzy inference system (ANFIS) to predict production and water productivity of lettuce in response to different light intensities and CO2 concentrations. Agricultural Water Management, 258, 107201. DOI

Ghorbanzadeh, P., Aliniaeifard, S., Esmaeili, M., Mashal, M., Azadegan, B., & Seif, M. (2020). Dependency of Growth, water use efficiency, chlorophyll fluorescence, and stomatal characteristics of lettuce plants to light intensity. Journal of Plant Growth Regulation, 40(5), 2191–2207. DOI

Hang, T., Lu, N., Takagaki, M., & Mao, H. (2019). Leaf area model based on thermal effectiveness and photosynthetically active radiation in lettuce grown in mini-plant factories under different light cycles. Scientia Horticulturae, 252, 113–120. DOI

Izzo, L. G., Mickens, M. A., Aronne, G., & Gómez, C. (2021). Spectral effects of blue and red light on growth, anatomy, and physiology of lettuce. Physiologia Plantarum, 172(4), 2191–2202. DOI

Ke, X., Yoshida, H., Hikosaka, S., & Goto, E. (2021). Optimization of photosynthetic photon flux density and light quality for increasing radiation-use efficiency in dwarf tomato under LED light at the vegetative growth stage. Plants, 11(1), 121. DOI

Kump, B. (2020). The role of far-red light (FR) in photomorphogenesis and its use in greenhouse plant production. Acta Agriculturae Slovenica, 116(1), 93–105. DOI

Kwack, Y., An, S., & Kim, S. K. (2021). Development of growth model for grafted hot pepper seedlings as affected by air temperature and light intensity. Sustainability, 13(11), 5895. DOI

Lee, M. J., Son, K. H., & Oh, M. M. (2016). Increase in biomass and bioactive compounds in lettuce under various ratios of red to far-red LED light supplemented with blue LED light. Horticulture, Environment, and Biotechnology, 57(2), 139–147. DOI

Loconsole, D., Cocetta, G., Santoro, P., & Ferrante, A. (2019). Optimization of LED lighting and quality evaluation of Romaine lettuce grown in an innovative indoor cultivation system. Sustainability, 11(3), 841. DOI

Marcos, L., & Mai, K. V. (2020). Light spectra optimization in indoor plant growth for internet of things. Proceedings of the International IOT, Electronics and Mechatronics Conference. DOI

Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. DOI

Mirjalili, S., & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51–67. DOI

Modarelli, G.C., Paradiso, R., Arena, C., De Pascale, S., & Van Labeke, M.-C. (2022). High light intensity from blue-red LEDs enhance photosynthetic performance, plant growth, and optical properties of red lettuce in controlled environment. Horticulturae, 8(2), 114. DOI

Mohamed, S. J., Rihan, H. Z., Aljafer, N., & Fuller, M. P. (2021). The impact of light spectrum and intensity on the growth, physiology, and antioxidant activity of lettuce (Lactuca sativa L.). Plants , 10(10). DOI

Monga, P., Sharma, M., & Sharma, S. K. (2021). A comprehensive meta-analysis of emerging swarm intelligent computing techniques and their research trend. In Journal of King Saud University - Computer and Information Sciences. King Saud bin Abdulaziz University. DOI

National Economic Development Authority. (2017). Updated Philippine Development Plan 2017-2022. Retrieved from website

Périard, Y., Caron, J., Lafond, J. A., & Jutras, S. (2015). Root water uptake by Romaine lettuce in a muck soil: Linking tip burn to hydric deficit. Vadose Zone Journal, 14(6), vzj2014.10.0139. DOI

Tarakanov, I. G., Tovstyko, D. A., Lomakin, M. P., Shmakov, A. S., Sleptsov, N. N., Shmarev, A. N., Litvinskiy, V. A., & Ivlev, A. A. (2022). Effects of light spectral quality on photosynthetic activity, biomass production, and carbon isotope fractionation in lettuce, Lactuca sativa L., Plants. Plants (Basel, Switzerland), 11(3). DOI

Urairi, C., Shimizu, H., Nakashima, H., Miyasaka, J., & Ohdoi, K. (2017). Optimization of light-dark cycles of Lactuca sativa L. in plant factory. Environmental Control in Biology, 55(2), 85–91. DOI

Valenzuela, I., Baldovino, R., Bandala, A., & Dadios, E. (2018). Pre-harvest factors optimization using genetic algorithm for lettuce. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1–4), 159–163. website

Valenzuela, I. C., Baldovino, R. G., Bandala, A. A., & Dadios, E. P. (2017). Optimization of photosynthetic rate parameters using Adaptive Neuro-Fuzzy Inference System (ANFIS). 2017 International Conference on Computer and Applications, 129–134. DOI

Zhangzhong, L., Gao, H., Zheng, W., Wu, J., Li, J., & Wang, D. (2023). Development of an evapotranspiration estimation method for lettuce via mobile phones using machine vision: Proof of concept. Agricultural Water Management, 275, 108003. DOI

Zou, J., Zhou, C. bo, Xu, H., Cheng, R. feng, Yang, Q. chang, & Li, T. (2020). The effect of artificial solar spectrum on growth of cucumber and lettuce under controlled environment. Journal of Integrative Agriculture, 19(8), 2027–2034. DOI

Zou, T., Huang, C., Wu, P., Ge, L., & Xu, Y. (2020). Optimization of artificial light for spinach growth in plant factory based on orthogonal test. Plants, 9(4), 490. DOI




DOI: http://doi.org/10.17503/agrivita.v45i2.3786

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