Transforming Agriculture with Machine Learning: Exploring Advanced Classification Methods

Abdullah Farag, Abdulrahman; H.Rizk, Faris; M.Eid, Marwa; Ibrahim, Abdelhameed; Abdelhamid, Abdelaziz A.; Sami Khafaga, Doaa; Ali Alhussan, Amel Ali Alhussan; A. Mashaal, Ahmed; M. EL-Kenawy, EL-Sayed

doi:10.21608/jassd.2024.283051.1017

Transforming Agriculture with Machine Learning: Exploring Advanced Classification Methods

Document Type : Original Article

Authors

¹ Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt

² Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA

³ Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt

⁴ School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain

⁵ Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, 11566, Cairo, Egypt

⁶ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

⁷ Department of Financial and Accounting Management Programs, Applied College, Princess Nora bint Abdul Rahman University, Saudi Arabia

10.21608/jassd.2024.283051.1017

Abstract

Agriculture has been one of the essential aspects of people’s lives since the beginning. Although it has remained basically the same, it has had to adapt to new demands. More humans are coming into this world, and environmental issues are getting worse. Such a situation makes the agriculture industry the most burdened with the responsibility of not exhausting resources and also increasing production levels. In light of advancements in technology and data processing, precision agriculture has become one way of coping with the issues of rising inputs. Machine learning and algorithmic methods can help farmers make the right decisions, use and optimize their resources, and reduce the risks associated with traditional farming. This paper sheds light on how a local farm and machine learning collaborate through the design of a classification method involving soil, weed, food, and animal management. Case papers and studies are among the other tools that show that classification in agriculture has several different areas of use. It also explores the possibility of the classification affecting the dynamics of agricultural functions to realize the utmost future security.

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