Machine Learning in Agriculture: Crop Yield Prediction: A contribution towards achieving zero hunger
$ 42.5
Description
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.