Enhancing Agricultural Productivity through Machine Learning: A Model for Accurate Crop Yield Prediction - adlin jebaumari; jayanthila devi (JES: Journal of Engineering Sciences)
Agriculture plays a pivotal role in sustaining human life, but challenges such as population growth, climate change, and resource competition threaten food security. To address these complexities in agricultural production, the concept of intelligent or smart farming has emerged, integrating technology into traditional agricultural practices. Machine learning, in particular, has become a crucial technology in agriculture, safeguarding food security and sustainability. By leveraging technology, farmers can enhance productivity and efficiency, and one key application of this is crop yield prediction. This paper focuses on predicting crop yield using various factors such as area, yield, production, and area under irrigation. The primary objective is to utilise machine learning techniques to forecast crop yield accurately, thereby aiding farmers in making informed decisions to optimize agricultural output and ensure food security.In the majority of developing countries, agriculture is the backbone of the economy. In India, agriculture is the biggest single-segment contributor to the economy, with a 13.05% share of the total Gross Domestic Product (GDP), and approximately 55% of the total households are dependent on agriculture.