摘要
为应对全球食品需求的增长和气候变化对传统农业的影响,开发了一种基于多智能算法融合的设施番茄最佳温度决策方法。设计了番茄嵌套试验试验,采集了不同环境条件下苗期番茄的光合速率数据,并建立光合速率预测模型;通过对该模型实例化获取光合速率随温度变化的响应函数,并通过导数理论完成最佳温度提取;最后设计了模拟试验验证了最佳温度条件在番茄幼苗光合积累上的优势。模型评价结果表明:光合速率预测模型在未知数据集上决定系数达0.99,均方根误差为1.37μmol/(m^(2)·s),较其他经典模型具有显著优势;模型实例化函数显示不同环境条件下皆存在一个最适宜的温度点,该温度可使番茄光合速率达到最大;模拟生产结果显示,一个生产日内单位面积番茄幼苗的光合积累达0.2166 mol/m^(2),较自然生长的番茄光合积累提升了一倍。综上,本决策方法提升了番茄的光合积累,为现代农业生产提供了有效的技术支持。
To address the global increase in food demand and the impact of climate change on traditional agriculture,we developed an optimal temperature decision-making method for protected-land tomato cultivation.Firstly,a nested experiment was designed for tomato seedlings,and photosynthesis rate data of tomato seedlings were collected under different environmental conditions,and a photosynthesis rate prediction model was established.By instantiating this model,we derived a response function illustrating how the photosynthesis rate changes with temperature,and identified the optimal temperature using derivative theory.Simulated experiments verified the benefits of optimal temperature conditions for the photosynthetic accumulation of tomato seedlings.Model evaluation results showed that the coefficient of determination of the photosynthesis rate prediction model on unknown datasets reached 0.99 with a root mean square error of 1.37μmol/(m^(2)·s),significantly outperforming other classic models.The model instantiation function showed that there was an optimal temperature point under different environmental conditions,which could maximize the photosynthesis rate of tomatoes.Simulated production results show that within a production day,the photosynthetic accumulation of tomato seedlings per unit area reached 0.2166 mol/m 2,doubling that of naturally grown tomatoes.The decision-making method proposed in this paper not only enhances the photosynthetic accumulation of tomatoes but also provides effective technical support for modern agricultural production.
作者
吴清丽
高攀
高茂盛
WU Qingli;GAO Pan;GAO Maosheng(Shaanxi Provincial Meteorological Information Center,Xi’an 710015,China;China Meteorological Administration Meteorological Cadre Training Center(Shaanxi),Xi’an 710015,China;Northwest A&F University,College of Mechanical and Electronic Engineering,Yangling Shaanxi 712100,China;Yangling Meteorological Bureau,Yangling Shaanxi 712100,China)
出处
《西北农业学报》
CAS
CSCD
北大核心
2024年第9期1795-1805,共11页
Acta Agriculturae Boreali-occidentalis Sinica
基金
秦岭和黄土高原生态环境气象重点实验室开放研究基金(2023K-3
2021G-9)。
关键词
神经网络
遗传算法
温度决策
光合速率
Neural network
Genetic algorithm
Temperature decision-making
Photosynthetic rate
