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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.

Autor: Ali T; University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan., Rehman SU; University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan., Ali S; University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi, Pakistan., Mahmood K; Institute of Computing and Information Technology, Gomal University, D.I. Khan, Pakistan., Obregon SA; Universidad Europea del Atlántico., Isabel Torres 21, Santander, 39011, Spain.; Universidad Internacional Iberoamericana, Campeche, 24560, Mexico.; Universidad Internacional Iberoamericana Arecibo, Puerto Rico, 00613, USA., Iglesias RC; Universidad Europea del Atlántico., Isabel Torres 21, Santander, 39011, Spain.; Universidade Internacional do Cuanza, Cuito, Bie, Angola.; Universidad de La Romana, La Romana, República Dominicana., Khurshaid T; Department of Electrical Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. tahir@ynu.ac.kr., Ashraf I; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea. imranashraf@ynu.ac.kr.

Publikováno v: Scientific reports [Sci Rep] 2024 Dec 03; Vol. 14 (1), pp. 30062. Date of Electronic Publication: 2024 Dec 03.