期刊论文

Ramie Yield SSA-BP Prediction Model Based on Climate Variables

中文

中国农业科技导报

1008-0864

2024

26

1

110-118

10.13304/j.nykjdb.2023.0557

2021JJ60011:湖南省自然科学基金项目 31471543:国家自然科学基金

本校为其他机构

苎麻产量与生长期间的气候因子具有极高相关性,基于气候变量构建的苎麻产量预测模型能够有效精准预测最终产量.BP(back propagation)神经网络具有强大的数据分析能力,在作物产量预测建模中得到广泛应用,然而传统BP神经网络存在精度低、鲁棒性差等问题,可采用麻雀搜索算法(sparrow search algorithm,SSA)对BP神经网络模型进行优化.基于2010-2019年苎麻长期定位试验采集的纤维产量、鲜皮产量和气候数据,分析气候因子在10年内的变化趋势及其对多年生苎麻产量的影响,对比构建的BP神经网络模型及优化后的SSA-BP神经网络模型预测苎麻产量的性能,确定最佳的苎麻产量预测模型.结果表明,苎麻产量与季平均...

The yield of ramie has a high correlation with the climate factors during the growth period,and the final yield can be effectively and accurately predicted by constructing a ramie yield prediction model based on climate variables.The BP neural network has strong data analysis capabilities and is widely used in crop yield prediction modeling.However,traditional BP neural networks have problems such as low accuracy and poor robustness.The sparrow search algorithm(SSA)can be used to optimize the BP neural network model.Based on the fiber yield,fresh skin yield and climate data collected in ramie ...