基于Logistic模型的设施番茄生长过程数字化研究

Digital research on growth process of facility tomato based on Logistic model

针对设施番茄在种植管理过程中长势难量化、精细化和标准化、栽培管理水平低等问题,以山东省德州市陵城区糜镇智慧农业产业园和山东农业大学科技创新园番茄生长数据为例,连续观察番茄植株的生长发育过程,利用Logistic方程分别对其地上部株高、茎粗、叶片的叶长、叶宽以及叶面积指数的生长动态过程进行拟合,获得设施番茄Logistic生长模型及生长特征参数,通过分析各性状的生长特征参数,划分番茄植株的生长期。结果表明:番茄不同生长指标的生长规律均符合S形生长曲线。拟合方程的决定系数R^(2)均≥0.853,定植天数与番茄各性状的相关系数R均≥0.923,达极显著水平(P<0.01)。根据S形生长曲线和番茄生长特点可将番茄生长阶段划分为生长前期、速生期和生长后期,不同性状速生期的起始和持续时间存在差异。采用Logistic模型可准确拟合番茄的生长动态过程,可为后期番茄生产种植过程中的数字化管理提供科学依据。

In view of the problems of difficult quantification,refinement and standardization of plant growth and low level of cultivation management of facility tomato,taking tomato growth data in Mi Town Smart Agriculture Industrial Park and Shandong Agricultural University Science and Technology Innovation Park in Lingcheng District,Dezhou City,Shandong Province as examples,the growth and development process of tomato plants was continuously observed.Logistic equation was used to fit the growth dynamic process of aboveground plant height,stem diameter,leaf length,leaf width and leaf area index,respectively,and the logistic growth model and growth characteristic parameters of facility tomato were obtained.The growth period of tomato plants was divided by analyzing the growth characteristic parameters of each trait,the results showed that the growth rulesof different growth indexes of tomato all conformed to the S-shaped growth curve.The coefficients of determination(R^(2))of the fitting equations were all≥0.853,and the correlation coefficients(R)between the days of planting and various tomato traits were all≥0.923,reaching a very significant level(P<0.01).According to the S-shaped growth curve and the growth characteristics of tomato,the growth stages of tomato can be divided into early growth stage,fast growing stage and late growth stage.There are differences in the start and duration of fast growing stage for different characters.The use of Logistic model can accurately fit the dynamics of tomato growth process,which can provide a scientific basis for the digital management of tomato production and planting in the later stage.