您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Modelling daily plant growth response to environmental conditions in Chinese solar greenhouse using Bayesian neural network

文献类型： 外文期刊

作者： Mohmed, Gadelhag 1 ; Heynes, Xanthea 1 ; Naser, Abdallah 2 ; Sun, Weituo 1 ; Hardy, Katherine 1 ; Grundy, Steven 1 ; Lu, Chungui 1 ;

作者机构： 1.Nottingham Trent Univ, Sch Anim Rural & Environm Sci, Brackenhurst Campus, Nottingham NG25 0QF, England

2.Nottingham Trent Univ, Dept Comp Sci, Clifton Campus, Nottingham NG11 8NS, England

3.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

期刊名称：SCIENTIFIC REPORTS （ 影响因子：4.6； 五年影响因子：4.9 ）

ISSN： 2045-2322

年卷期： 2023 年 13 卷 1 期

页码：

收录情况： SCI

摘要： Understanding how plants respond to environmental conditions such as temperature, CO2, humidity, and light radiation is essential for plant growth. This paper proposes an Artificial Neural Network (ANN) model to predict plant response to environmental conditions to enhance crop production systems that improve plant performance and resource use efficiency (e.g. light, fertiliser and water) in a Chinese Solar Greenhouse. Comprehensive data collection has been conducted in a greenhouse environment to validate the proposed prediction model. Specifically, the data has been collected from the CSG in warm and cold weather. This paper confirms that CSG's passive insulation and heating system was effective in providing adequate protection during the winter. In particular, the CSG average indoor temperature was 18 C-? higher than the outdoor temperature. The difference in environmental conditions led to a yield of 320.8g per head in the winter after 60 growing days compared to 258.9g in the spring experiment after just 35 days. Three different architectures of Bayesian Neural Networks (BNN) models have been evaluated to predict plant response to environmental conditions. The results show that the BNN network is accurate in modelling and predicting crop performance.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台