Transforming Agriculture with Machine Learning: Exploring Advanced Classification Methods | ||
| Journal of Agricultural Sciences and Sustainable Development | ||
| Volume 1, Issue 3, September 2024, Pages 243-254 PDF (609.1 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jassd.2024.283051.1017 | ||
| Authors | ||
| Abdulrahman Abdullah Farag1; Faris H.Rizk2; Marwa M.Eid3; Abdelhameed Ibrahim4; Abdelaziz A. Abdelhamid5; Doaa Sami Khafaga6; Amel Ali Alhussan Ali Alhussan6; Ahmed A. Mashaal7; EL-Sayed M. EL-Kenawy* 1 | ||
| 1Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura 35111, Egypt | ||
| 2Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA | ||
| 3Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35111, Egypt | ||
| 4School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain | ||
| 5Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, 11566, Cairo, Egypt | ||
| 6Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia | ||
| 7Department of Financial and Accounting Management Programs, Applied College, Princess Nora bint Abdul Rahman University, Saudi Arabia | ||
| 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. | ||
| Keywords | ||
| Precision agriculture; Machine learning; Classification; Crop management | ||
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