基于KNN-SVM算法的温室番茄生长预测模型研究

Research on greenhouse tomato growth prediction model based on KNN-SVM algorithm

随着我国农业温室大棚技术的发展,番茄成为大棚蔬菜中典型的农作物之一,我国番茄种植生产量以及规模已位于世界第一。就目前数据显示,我国番茄大棚种植管理数据可视化程度较低,在番茄生产种植作业中,对其生长所需的参数难以精确调控,严重影响大棚作物产业的进一步发展。为确保大棚番茄生产作业的科学化、精准化,文章提出了基于KNN-SVM算法的温室番茄生长预测模型,通过模型,结合信息化设备以及人工方式采集的大棚番茄全周期生长信息,生成大棚番茄各阶段生长预测模型。在项目部署过程中,从算法的改进、数据采集、数据分析、数据可视化等方面入手,使得其适应环境的能力更强,数据的准确性更高,为大棚番茄规范化种植提供了参考。

With the development of greenhouse technology in agriculture in China,tomatoes have become one of the typical crops in greenhouse vegetables.The production and scale of tomato cultivation in China have ranked first in the world.According to current data,the visualization level of tomato greenhouse planting management data in China is relatively low.In tomato production and planting operations,it is difficult to precisely control the parameters required for its growth,which seriously affects the further development of the greenhouse crop industry.To ensure the scientific and accurate production of greenhouse tomatoes,the article proposes a greenhouse tomato growth prediction model based on KNN-SVM algorithm.Through the model,combined with information equipment and manual collection of greenhouse tomato full cycle growth information,a growth prediction model for each stage of greenhouse tomatoes is generated.In the project deployment process,starting from algorithm improvement,data collection,data analysis,data visualization,and other aspects,the ability to adapt to the environment is stronger,and the accuracy of data is higher,providing a reference for standardized cultivation of greenhouse tomatoes.