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

Maryam Esmaili, Sasan Aliniaeifard, Mahmoud Mashal, Keyvan Asefpour Vakilian, Parisa Ghorbanzadeh, Behzad Azadegan, Mehdi Seif and Fardad Didaran

Agricultural Water Management, 2021, vol. 258, issue C

Abstract: The accurate estimation of water productivity (WP) and plant production becomes imperative in planning and managing irrigation practices. Light intensity and CO2 concentration are among the most important determinants of growth and WP of crops. In this study, the adaptive neuro-fuzzy inference system (ANFIS) was used to model the changes in growth parameters, stomatal properties, and WP of lettuce due to various scenarios of light intensity and CO2 concentration. The lettuce plants were exposed to four levels of light intensity [75, 150, 300, and 600 µmol m−2 s−1 Photosynthetic Photon Flux Density (PPFD)] and CO2 concentration (400, 800, 1200, and 1600 ppm). The results showed that growth parameters such as fresh weight, dry weight, and leaf area improved by increasing the PPFD and CO2 concentration from 75 to 300 µmol m−2 s−1 and 400–1200 ppm, respectively. Maximum fresh weight was recorded at 300 µmol m−2 s−1 PPFD and 1600 ppm CO2 concentration while the highest dry weight was obtained at 600 µmol m−2 s−1 PPFD and 1600 ppm CO2 concentration. Stomatal pore width and length decreased by increasing PPFD and CO2 concentration. Moreover, evapotranspiration increased when plants were exposed to higher PPFDs and CO2 concentrations. ANFIS predicted all growth parameters, stomatal properties, and WP with acceptable performance (R2 > 0.99, RMSE < 0.8 ×10−2). The findings provide agricultural engineers with an artificial intelligence-based model to predict the WP and production of lettuce by having the light intensity and CO2 concentration.

Keywords: Artificial intelligence; Crop performance; Environmental cues; Photosynthetic photon flux density (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377421004789
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:258:y:2021:i:c:s0378377421004789

DOI: 10.1016/j.agwat.2021.107201

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().