In the agricultural sector, crop yield prediction plays an important role as it helps farmers make decisions about the growing season and type of crops to get better yield. The main goal in the agricultural sector is to reduce operating costs and pollution by improving crop yields and quality. This paper proposes an effective method for soil analysis and crop yield prediction for intelligent agriculture. The collected data are preprocessed using missing value interpolation and data normalisation techniques. Feature selection is performed on the preprocessed data using the Aquila‐based adaptive optimisation algorithm, which selects the best trait subset for yield prediction. An improved lightweight gradient‐boosting machine based on the Battle Royale Optimisation technique is used for classification. The performance of the proposed system is evaluated using mean absolute error, root mean square error, R‐squared, mean square error, mean square logarithmic error and mean absolute percentage error, and the proposed system achieved an accuracy of 97%. The proposed system accurately predicts crop yields, improving crop production and quality.

中文翻译：

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智能农业中基于优化 Light GBM 的有效土壤分析和作物产量预测


在农业领域，作物产量预测发挥着重要作用，因为它可以帮助农民决定作物的生长季节和类型以获得更好的产量。农业部门的主要目标是通过提高农作物产量和质量来降低运营成本和污染。本文提出了一种有效的智能农业土壤分析和作物产量预测方法。使用缺失值插值和数据标准化技术对收集的数据进行预处理。使用基于 Aquila 的自适应优化算法对预处理数据进行特征选择，该算法选择最佳性状子集进行产量预测。基于大逃杀优化技术的改进的轻量级梯度提升机用于分类。使用平均绝对误差、均方根误差、R平方、均方误差、均方对数误差和平均绝对百分比误差来评估所提出系统的性能，所提出系统的准确度达到97%。该系统可以准确预测作物产量，提高作物产量和质量。