Editorial: Continuous living cover: adaptive strategies for putting regenerative agriculture into practice

Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach

“…This study will open the door for further, in-depth research into this and other KSArelated decision issue domains. Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL), like the models used in Ghaderzadeh et al (2022); Habib et al, 2023;Humayun et al, 2021;Jabbari et al, 2023;Ju-Long, 1982;Jyoti Deogirikar, 2017;Kabak and Dagdeviren, 2014;Kaushal et al, 2022;Kaya and Kahraman, 2010;Lee, 2020;Likert, 1985;Luthra et al, 2018;Madakam et al, 2015;Masoodi et al, 2019;Masoodi et al, 2021;Mohammed et al, 2021;Nguyen et al, 2022;Nguyen et al, 2021;Palattella et al, 2013;Quasim, 2021;Rajesh and Ravi, 2015;Rambo et al, 2017;Ratnakumari and Koteswari, 2020;Rawal, 2017;Reilly et al, 2023;Saaty, 1989-Sadoughi andGhaderzadeh (2014), are often used to implement state-of-the-art proposals, such as machine vision techniques for guessing parameters of importance, such as humidity. Moreover, fuzzy logic is commonly mentioned in the literature associated with ML, ANNs, and DL, especially when fuzzy logic systems are used in irrigation control.…”