Data-driven rice yield predictions and prescriptive analytics for sustainable agriculture in Malaysia
Maximizing rice yield is critical for ensuring food security and sustainable agriculture in Malaysia. This research investigates the impact of environmental conditions and management methods on crop yields, focusing on accurate predictions to inform decision-making by farmers. Utilizing machine lear...
Main Authors: | Marong, Muhammad, Husin, Nor Azura, Zolkepli, Maslina, Affendey, Lilly Suriani |
---|---|
Format: | Article |
Language: | English |
Published: |
Science and Information Organization
2024
|
Online Access: | http://psasir.upm.edu.my/id/eprint/112884/1/112884.pdf |
Similar Items
-
Tutorial on prescriptive analytics for logistics: what to predict and how to predict
by: Tian, Xuecheng, et al.
Published: (2024) -
Data visualization for agricultural data: benefits and challenges
by: Sidi, Fatimah, et al.
Published: (2017) -
An overview of using analytics approach to predict internet usage and student performance in education: a proposed prescriptive analytic approach
by: Khamis, Shakiroh, et al.
Published: (2018) -
Prescriptive analytics models for vessel inspection planning in maritime transportation
by: Yang, Ying, et al.
Published: (2024) -
A system literature review on evolution of big data analytics application
by: Adrian, Cecilia, et al.
Published: (2015)