Crop yield Prediction and Recommendation Using Machine Learning

Authors- Dhivakar M, Assistant Professor, H Jayamangala

Abstract-The rise in population at a rapid rate and the impact of climate change have increased the challenge of achieving global food security. To meet the challenge, the project is aimed at developing a machine learning-based Crop Yield Prediction framework using significant parameters of agriculture like soil nutrient levels, weather conditions, and soil pH levels. The solution proposed uses past agricultural data to train a Support Vector Machine (SVM) model to predict crop yields effectively and give strategic advice in relation to Fertilizer, Irrigation, Maintenance, and Cultivation practices. The proposed system has a novel approach by combining predictive analytics with practical agricultural advisories and thereby enhancing decision-making strategies to maximize resources and foster sustainable agricultural practices. The overall objective is to empower farmers with predictive knowledge leading to enhanced productivity and improved resource allocation, ultimately fostering sustainable agricultural practices in the face of evolving environmental challenges.

DOI: /10.61463/ijset.vol.13.issue3.129