Agricultural data classification attracts more and more attention in the
research area of intelligent agriculture. As a kind of important machine
learning methods, ensemble learning uses multiple base classifiers to deal
with classification problems. The rough set theory is a powerful
mathematical approach to process unclear and uncertain data. In this paper,
a rough set based ensemble learning algorithm is proposed to classify the
agricultural data effectively and efficiently. An experimental comparison of
different algorithms is conducted on four agricultural datasets. The results
of experiment indicate that the proposed algorithm improves performance
obviously.