Tilime crop yield prediction using machine learning algorithms

Abstract

Agriculture stands as the bedrock of Malawi's economy, involving nearly 90% of the population in subsistence farming. However, the sector faces challenges arising from unpredictable weather patterns, climate shifts, and environmental factors that threaten its sustainability. This paper proposes a pioneering solution leveraging Machine Learning (ML) to address these challenges, presenting a robust decision support system for Crop Yield Prediction (CYP). By harnessing ML capabilities, the system aids in crucial decisions related to crop selection and management throughout the growing season, specifically tailored for the unique agricultural landscape of Malawi. This approach aims to empower farmers by providing valuable insights into soil quality, composition, and nutrients, enabling informed decisions to maximize crop yield. Through the integration of advanced technology into the agricultural domain, this paper seeks to usher in a transformative era for Malawian agriculture, fostering resilience and sustainability in the face of evolving environmental dynamics.