Machine Learning Framework to Enhance Agricultural Output Prediction

Precise agricultural output projection is becoming more and more crucial due to the rising need for food production to sustain the growing global population. Because environmental elements and farming practices are complicated, traditional methods frequently do not yield accurate estimates. A potential remedy is to use machine learning (ML) techniques, which use massive datasets to model complex relationships and more accurately anticipate agricultural output. The framework analyzes various agricultural data, including crop types, soil characteristics, Insects, and pesticide records. It integrates different machine learning methods, including supervised, unsupervised, and deep learning models. Using real-world agricultural datasets from various areas and crop types, the suggested framework is assessed by considering the performance parameters. Scalability and flexibility allow the framework to be adjusted to different agricultural situations while giving stakeholders valuable data to help with decision-making and resource allocation