基于不同叶位的茄子花期光合速率预测模型研究
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摘要
在研究作物不同叶位光合速率差异的基础上,以花期茄子植株为试验材料,设计嵌套试验,利用回归型支持向量机算法(SVR)实现多元非线性回归拟合,建立了一种融合不同叶位的光合速率预测模型,采用异校验方法对该模型进行验证分析。结果表明:考虑叶位影响下的光合速率预测值与实测值决定系数为0. 9960,均方误差为0. 2503,平均相对误差为5. 47%,平均绝对误差为0. 068。该试验结果为研究作物株间按需补光以及不同叶位高效智能补光奠定了良好基础。
On the basis of studying the difference in photosynthetic rate of leaves at different positions,the eggplant crops during flowering state were used as experimental materials in nested experiments,in support vector regression algorithm( SVR) in multivariate nonlinear regression analysis and a multi-leaf photosynthetic rate prediction model was established. The model was verified by different calibration methods. Results showed that affected by different leaf positions,the determination coefficient of photosynthetic rates between actual measured and predicted values reached 0. 996,predicted of 0. 2503,the mean relative error was 5. 47%,the mean absolute error was 0. 068. The findings lay a good foundation for practices of supplementing lighting between crops and for leaves of specific positions.
On the basis of studying the difference in photosynthetic rate of leaves at different positions,the eggplant crops during flowering state were used as experimental materials in nested experiments,in support vector regression algorithm( SVR) in multivariate nonlinear regression analysis and a multi-leaf photosynthetic rate prediction model was established. The model was verified by different calibration methods. Results showed that affected by different leaf positions,the determination coefficient of photosynthetic rates between actual measured and predicted values reached 0. 996,predicted of 0. 2503,the mean relative error was 5. 47%,the mean absolute error was 0. 068. The findings lay a good foundation for practices of supplementing lighting between crops and for leaves of specific positions.
引文
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[19]焦卫东,林树森.整体改进的基于支持向量机的故障诊断方法[J].仪器仪表学报,2015,36(8):1861-1870.
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[23]王建国,董泽宇,张文兴,等.基于回归树的支持向量机规则提取及应用[J].计算机工程与应用,2017,53(6):236-240,270.
[24]邬啸,魏延,吴瑕.基于混合核函数的支持向量机[J].重庆理工大学学报(自然科学),2011,25(10):66-70.
[2]艾希珍,张振贤,何启伟,等.日光温室黄瓜不同叶位叶片光合作用研究[J].中国农业科学,2002,35(12):1519-1524.
[3]韩清芳,贾志宽,王俊鹏,等.黄土高原地区紫花苜蓿不同叶位光合日变化特征研究[J].草地学报,2009,17(5):558-563.
[4]罗俊,张华,邓祖湖,等.甘蔗不同叶位叶片形态与冠层特征的关系[J].应用与环境生物学报,2005(1):28-31.
[5]王群瑛,胡昌浩.玉米不同叶位叶片叶绿体超微结构与光合性能的研究[J]. Journal of Integrative Plant Biology,1988,30(2):146-150,239.
[6]姜英,林叶春,许和水,等.两种C4作物不同叶位光合及叶绿素荧光特性比较[J].中国农业大学学报,2012,17(3):34-42.
[7]PETRIE,P R,TROUGHT M C T,HOWELL G S. Influence of leaf ageing,leaf area and crop load on photosynthesis,stomatal conductance and senescence of grapevine(Vitis vinifera L. cv. Pinot noir)leaves[J]. Vitis-Geilweilerhof,2000,39(1):31-36.
[8]SUZUKI Y J,MIYAMOTO T,YOSHIZAWA R,et al. Rubisco content and photosynthesis of leaves at different positions in transgenic rice with an overexpression of RBCS[J]. Plant,Cell and Environment,2010,32(4):417-427.
[9]ARAUJO W L,DIAS P C,MORAES G A B K,et al. Limitations to photosynthesis in coffee leaves from different canopy positions[J]. Plant Physiology and Biochemistry,2008,46(10):884-890.
[10]李伟,眭晓蕾,王绍辉,等.黄瓜幼苗不同叶位叶片光合特性对弱光的响应[J].中国农业科学,2008,41(11):3698-3707.
[11]常莹,闫伟平,李程,等.半干旱区玉米不同叶位光合特征研究[J].吉林农业科学,2014,39(2):1-4,17.
[12]张海辉,陶彦蓉,胡瑾.融合叶绿素含量的黄瓜幼苗光合速率预测模型[J].农业机械学报,2015,46(8):259-263,307.
[13]宋海声,赵学深,刘平和. LI-RBF算法在光合速率预测模型研究中的应用[J].计算机测量与控制,2013,21(10):2641-2644.
[14]王伟珍,刘春红,张漫,等.基于BP神经网络的温室番茄光合作用速率预测模型研究[C]//北京:中国农业工程学会(CSAE).中国农业工程学会2011年学术年会论文集,2011:4.
[15]高相铭,刘付斌,杨世凤.基于极限学习机的供水管网故障智能诊断方法[J].计算机工程与设计,2013,34(8):2887-2891.
[16]张宏,马岩,李勇,等.基于遗传BP神经网络的核桃破裂功预测模型[J].农业工程学报,2014,30(18):78-84.
[17]张海辉,张珍,张斯威,等.黄瓜初花期光合速率主要影响因素分析与模型构建[J].农业机械学报,2017,48(6):1-8.
[18]刘彤,武海巍,曹忠.基于支持向量机的水稻净光合速率预测模型[J].中国农机化学报,2016,37(9):151-153.
[19]焦卫东,林树森.整体改进的基于支持向量机的故障诊断方法[J].仪器仪表学报,2015,36(8):1861-1870.
[20]常甜甜.支持向量机学习算法若干问题的研究[D].西安:西安电子科技大学,2010.
[21]王国胜.支持向量机的理论与算法研究[D].北京:北京邮电大学,2008.
[22]张海刚,张森,尹怡欣.基于全局优化支持向量机的多类别高炉故障诊断[J].工程科学学报,2017,39(1):39-47.
[23]王建国,董泽宇,张文兴,等.基于回归树的支持向量机规则提取及应用[J].计算机工程与应用,2017,53(6):236-240,270.
[24]邬啸,魏延,吴瑕.基于混合核函数的支持向量机[J].重庆理工大学学报(自然科学),2011,25(10):66-70.