An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Hossein Dehghanisanij; Nader Salamati; Somayeh Emami; Hojjat Emami; Haruyuki Fujimaki

doi:10.1007/s13201-022-01836-8

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Appl Water Sci. 2023;13(2):56. doi: 10.1007/s13201-022-01836-8. Epub 2022 Dec 30.

Authors

Hossein Dehghanisanij¹, Nader Salamati², Somayeh Emami³, Hojjat Emami⁴, Haruyuki Fujimaki⁵

Affiliations

¹ Agricultural Research, Education and Extension Organization, Agricultural Engineering Research Institute, Karaj, P.O. Box 31585-845, Alborz Iran.
² Agricultural Research, Education and Extension Organization, Khuzestan Agricultural and Natural Resources Research and Education Center, Ahvaz, Iran.
³ Department of Water Engineering, University of Tabriz, Tabriz, Iran.
⁴ Department of Computer Engineering, University of Bonab, Bonab, Iran.
⁵ Arid Land Research Center, Tottori University, Tottori, Japan.

Abstract

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m³, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m³/ha, which suggests a decrease of 5019.20 and 2509.6 m³/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.

Keywords: ACVO algorithm; ACVO-ANFIS; Date palm; Subsurface drip irrigation (SDI); Water productivity; Yield.