ECP-IEM: Enhancing seasonal crop productivity with deep integrated models.

ABSTRACT: Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population and global warming, addressing food security has become a priority, so accurate yield forecasting is very important. Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model's leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. The proposed methodology consists of different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector

SUBMITTER: Mustafa G 

PROVIDER: S-EPMC11798460 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature