Abstract
The amount of crops harvested varies every year due to changes in climate and other operational as well as economic factors. Predicting the amount of crops a land will produce will result in more efficient field operations and management. At a national level, crop yield prediction can help work towards achieving food security. This, at a global level, will serve as a step towards the UN Sustainable Development Goal of Zero Hunger. This research identifies the significant factors that affect the production of staple crops in regions with desert and semi-arid climate in Africa and predict their yield. Different techniques are experimented to create the model and Random Forest proves to be the most suitable for this problem.
Publication Date
12-2022
Document Type
Master's Project
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Advisor
Sanjay Modak
Advisor/Committee Member
Khalil Al Hussaeni
Recommended Citation
Sherif, Hames, "Machine Learning in Agriculture: Crop Yield Prediction" (2022). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/11393
Campus
RIT Dubai
