In agriculture, optimizing crop yield and maintenance practices is
essential for ensuring food security and sustainable farming.
Traditional approaches often lack the efficiency needed to process
large agricultural datasets and accurately predict yield under varying
environmental conditions. This project leverages the Light Gradient
Boosting Machine (LightGBM), a high-performance, gradient-
boosting framework specifically designed for large-scale data
handling, to address the challenge of yield prediction and crop
maintenance optimization. By integrating LightGBM, which handles
heterogeneous data with high accuracy, we aim to enhance predictions
on crop yield while minimizing resource use. The proposed method
analyzes a range of factors, including soil quality, weather conditions,
irrigation practices, and historical crop yield records. Initial results
indicate that LightGBM outperforms conventional models with a
94.7% accuracy rate in yield prediction and reduces maintenance costs
by up to 20% by recommending optimized agricultural practices based
on specific environmental conditions. These findings underscore the
potential of LightGBM as an effective tool in precision agriculture,
ultimately aiding farmers in making informed decisions and improving
agricultural productivity.