Scholarlink

Purpose <p>In order to improve productivity, it is necessary to analyze and consider the relationships between growth data, environmental data, and various factors collected from smart farms and cultivate under optimal growth environmental conditions. This study aimed to analyze the correlation between tomato growth data and environmental data collected from semi-closed greenhouses, a new type of smart farm, and investigate the impact of each data variable on tomato yield through multiple regression analysis models to establish a foundation for finding optimal cultivation environmental conditions for tomatoes in semi-closed greenhouses.</p> Method <p>Data was collected from two different types of greenhouses: a glass greenhouse with natural ventilation and a semi-closed greenhouse with a forced-air circulation system. The collection periods were 30&#xa0;weeks for the glass greenhouse and 40&#xa0;weeks for the semi-closed greenhouse. Nine growth variables and two environmental variables were measured weekly. The collected data was analyzed using correlation analysis and multiple regression analysis with multicollinearity diagnostics to identify significant relationships and interactions between variables.</p> Result <p>Multiple regression analysis revealed distinct patterns in each greenhouse type. In the glass greenhouse, flowering clusters demonstrated the strongest correlation with fruit yield, while in the semi-closed greenhouse, fruit yield was best modeled using average temperature, stem thickness, and cumulative solar radiation. Multicollinearity diagnostics identified issues with fruit-setting clusters in the glass greenhouse, while the semi-closed greenhouse showed mild multicollinearity issues with cumulative solar radiation. The semi-closed greenhouse was more effective at regulating temperatures than the glass greenhouse due to a combination of factors that facilitated precise environmental control.</p> Conclusions <p>Based on the research findings, improving model stability requires collecting data on various variables, investigating their correlations, making accurate variable selections, and mitigating multicollinearity issues. These research findings can serve as foundational data for future studies and are expected to contribute to improving agricultural productivity. The semi-closed greenhouse’s more complex correlation patterns suggest that it offers greater potential for precise environmental control, but also requires more sophisticated management approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Effects of Environmental Factors of Large-Scale Semi-closed Greenhouse on Growth Characteristics of Tomato

  • 期刊论文

  • 发表日期：

    2025年2月14日

查看全文
  • Chaehong An,
  • Sungwan Park,
  • Hoon Seonwoo

摘要

Purpose

In order to improve productivity, it is necessary to analyze and consider the relationships between growth data, environmental data, and various factors collected from smart farms and cultivate under optimal growth environmental conditions. This study aimed to analyze the correlation between tomato growth data and environmental data collected from semi-closed greenhouses, a new type of smart farm, and investigate the impact of each data variable on tomato yield through multiple regression analysis models to establish a foundation for finding optimal cultivation environmental conditions for tomatoes in semi-closed greenhouses.

Method

Data was collected from two different types of greenhouses: a glass greenhouse with natural ventilation and a semi-closed greenhouse with a forced-air circulation system. The collection periods were 30 weeks for the glass greenhouse and 40 weeks for the semi-closed greenhouse. Nine growth variables and two environmental variables were measured weekly. The collected data was analyzed using correlation analysis and multiple regression analysis with multicollinearity diagnostics to identify significant relationships and interactions between variables.

Result

Multiple regression analysis revealed distinct patterns in each greenhouse type. In the glass greenhouse, flowering clusters demonstrated the strongest correlation with fruit yield, while in the semi-closed greenhouse, fruit yield was best modeled using average temperature, stem thickness, and cumulative solar radiation. Multicollinearity diagnostics identified issues with fruit-setting clusters in the glass greenhouse, while the semi-closed greenhouse showed mild multicollinearity issues with cumulative solar radiation. The semi-closed greenhouse was more effective at regulating temperatures than the glass greenhouse due to a combination of factors that facilitated precise environmental control.

Conclusions

Based on the research findings, improving model stability requires collecting data on various variables, investigating their correlations, making accurate variable selections, and mitigating multicollinearity issues. These research findings can serve as foundational data for future studies and are expected to contribute to improving agricultural productivity. The semi-closed greenhouse’s more complex correlation patterns suggest that it offers greater potential for precise environmental control, but also requires more sophisticated management approaches.