PREDICTION OF CROP YIELD USING MACHINE LEARNING TECHNIQUE
Abstract
This abstract introduces a Crop Prediction Model utilizing Machine Learning (ML) algorithms for accurate crop yield forecasts. By amalgamating historical and real-time data on climate, soil quality, crop varieties, and agricultural practices, this model employs various ML techniques, including Random Forest, Support Vector Machines, and Neural Networks, to predict crop yields. It accommodates spatial and temporal variations to adapt to diverse regions and changing climate conditions. The model's promising results in accuracy and scalability hold significant potential for farmers, researchers, and policy-makers, enabling proactive planning, efficient resource allocation, and risk mitigation, thus fostering increased agricultural productivity and sustainability. In essence, this research underscores the transformative impact of Machine Learning on crop yield prediction, facilitating informed decision-making, waste reduction, and global food security.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.