|Table of Contents|

Construction of Lettuce Growth Model Based on Growing Degree Days

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2024年6
Page:
9-16
Research Field:
Publishing date:

Info

Title:
Construction of Lettuce Growth Model Based on Growing Degree Days
Author(s):
DUAN Guangjun1ZHAO Jiasong1LIU Zhenyang2YAN Weiyu1MA Dianjing1WANG Lijia1
(1.Big Data College,Yunnan Agricultural University,Kunming,Yunnan 650201;2.College of Data Science and Engineering,Kunming City College,Kunming,Yunnan 650032)
Keywords:
growth modelLogistic regressionridge regressionsupport vector regression
PACS:
S 636.2
DOI:
10.11937/bfyy.20233874
Abstract:
Taking lettuce as the test material,using three algorithms,Logistic regression,ridge regression and support vector regression,the fitting effect and prediction ability of different algorithms on the lettuce growth model were studied,in order to provide reference for effective planning,growth prediction and yield-increasing measures for subsequent lettuce production.The results showed that the average R2 of the measured values and predicted values of the lettuce growth model of Logistic regression,ridge regression and support vector regression based on the 1∶1 straight line were 0.818,0.897 and 0.957 respectively,and the average RMSE were 27.631,19.505 and 6.901,respectively.Therefore support vector regression performed the best in simulating lettuce growth,followed by ridge regression,and Logistic regression was the worst.

References:

[1]陈永快,黄语燕,兰婕,等.基于辐热积的NFT栽培生菜生长模型[J].江苏农业科学,2021,49(19):201-204,215.[2]de WIT C T.Photosynthesis of leaf canopies[M].Wageningen:Centre for Agricultural Publications and Documentation,1965.[3]HEUVELINK E.Dry matter partitioning in tomato:validation of a dynamic simulation model[J].Annals of Botany,1996,77(1):71-80.[4]GIJZEN H,HEUVELINK E,CHALLA H,et al.Hortisim:A model for greenhouse crops and greenhouse climate[J].Acta Horticulturae,1998(456):441-450.[5]高亮之,金之庆,黄耀,等.水稻计算机模拟模型及其应用之一 水稻钟模型:水稻发育动态的计算机模型[J].中国农业气象,1989,10(3):3-10.[6]黄策,王天铎.水稻群体物质生产过程的计算机模拟[J].作物学报,1986,12(1):1-8.[7]殷新佑,戚昌瀚.水稻生长日历模拟模型及其应用研究[J].作物学报,1994,20(3):339-346.[8]高亮之,金之庆,黄耀,等.作物模拟与栽培优化原理的结合-RCSODS[J].作物杂志,1994(3):4-7.[9]陈杨.有效积温与夏玉米生长发育和氮磷钾积累定量化研究[D].北京:中国农业科学院,2021.[10]陈永快,黄语燕,王涛,等.基于有效积温的NFT栽培小白菜生长模型[J].江苏农业科学,2020,48(17):229-233.[11]于志民,康文娟,涂淑萍.基于Logistic、Gompertz模型的圆齿野鸦椿幼苗生长模拟与分析[J].江西农业大学学报,2017,39(6):1187-1195.[12] 陈琦,潘好芹,亓延凤,等.不同LED补光对日光温室黄瓜生长、产量及品质的影响[J].北方园艺,2022(21):50-57.[13] 谷端银,常青,王晓云,等.日光温室秋冬茬不同品种樱桃番茄生长及品质特性研究[J].北方园艺,2022(18):46-51.[14]洪苗,柳平增,张艳,等.基于主要环境因子的设施黄瓜生长模型研究[J].中国农机化学报,2022,43(4):32-37.[15]李德,陈文涛,乐章燕,等.基于随机森林算法和气象因子的砀山酥梨始花期预报[J].农业工程学报,2020,36(12):143-151.[16]曾妍,王迪,赵小娟.基于支持向量回归的关中平原冬小麦估产研究[J].中国农业信息,2019,31(6):10-20.[17]刘小锐,黄成东,祝红伟.叶用莴苣叶面积测定方法的研究[J].中国蔬菜,2020(12):78-81.[18]李书钦,诸叶平,刘海龙,等.冬小麦返青后叶片高度模型构建及三维可视化[J].中国农业科技导报,2017,19(11):59-67.[19]朱尚伟,李景华.岭回归估计的向量参数方法[J].应用概率统计,2018,34(5):501-514.[20]杨楠.岭回归分析在解决多重共线性问题中的独特作用[J].统计与决策,2004(3):14-15.[21]武琼.基于支持向量回归的短时交通流预测方法研究与应用[D].西安:长安大学,2016.[22]殷志奇.基于气象数据的柳州甘蔗产量预测研究[D].柳州:广西科技大学,2021.

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Last Update: 2024-04-11