Crop Yield Recommendation Using Machine Learning

Crop forecasting constitutes an integral part of the modern farming practice, affecting the decisions of farmers and agricultural companies. This machine learning model is intended to produce more accurate yield forecast with multiple datasets including weather temperature, pesticides, location and other as well. The methodology that systematizes data acquirement, preprocessing, model selection, training and implementing is used by the project to overcome many obstacles in crop production. Accurate performance forecasting in this technology is therefore not only a matter of innovation, but of food security, economy and environment as well. The model training & testing is done with mean absolute error to determine its accuracy & generalization. After training the model is launched into the production environment or plenty into the existing agricultural platforms for real-time yield forecasting. This project will constantly be striving for improvement and filter knowledge down to the stakeholders, optimizes resources allocation and brings about change entrenching food security and sustainable development.