000			a
999			_c22704 _d22704
003			OSt
005			20250424103931.0
008			250424b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925451 _a Kumar, Sunil
245			_aLight gradient boosting machine for optimizing crop maintenance and yield prediction in Agricultur
250			_aVol.15(2), Oct
260			_aChennai _bICT Academy _c2024
300			_a3551-3555p.
520			_aIn 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.
650		0	_94622 _aComputer Engineering
700			_925968 _aMohammed Ali Sohail
773	0		_dChennai ICT Academy _tICTACT Journal on Soft Computing (IJSC)
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_11_3551_3555.pdf _yClick here
942			_2ddc _cAR