Scholarlink

<p>Estimation of plant transpiration is essential for developing efficient irrigation strategies and improving crop management. However, delineating water flow within complex crop production systems is challenging. Neural networks can model complicated relationships without manual feature extraction; therefore, they can help in extracting transpiration rates from greenhouse data. This study introduces a quantile regression approach to predict transpiration rates using the Penman–Monteith (PM) equation in conjunction with long short-term memory (LSTM) networks. Data from load cell scales were used to collect the weight measurements, and weight-based transpiration rates were calculated to calibrate the PM equation. The quantile regressor was applied at various quantiles (0.1, 0.3, 0.5, 0.7, and 0.9) using the LSTM model and demonstrated acceptable accuracy with an R<sup>2</sup> of 0.56 and root mean square error (RMSE) of 0.15&#xa0;g m<sup>− 2</sup> min<sup>− 1</sup>, which was slightly better than those of multivariate regression (R<sup>2</sup>= 0.53, RMSE = 0. 16&#xa0;g m<sup>− 2</sup> min<sup>− 1</sup>). The proposed model provided highly robust transpiration rate predictions than multivariate regression. Utilizing quantile regression with neural networks can improve water management and optimize resource use in controlled environments.</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

Predicting transpiration of tomato in greenhouse using quantile regression with the Penman–Monteith equation and neural networks

  • 期刊论文

  • 发表日期：

    2026年2月13日

查看全文
  • Taewon Moon,
  • Joonwoo Lee

摘要

Estimation of plant transpiration is essential for developing efficient irrigation strategies and improving crop management. However, delineating water flow within complex crop production systems is challenging. Neural networks can model complicated relationships without manual feature extraction; therefore, they can help in extracting transpiration rates from greenhouse data. This study introduces a quantile regression approach to predict transpiration rates using the Penman–Monteith (PM) equation in conjunction with long short-term memory (LSTM) networks. Data from load cell scales were used to collect the weight measurements, and weight-based transpiration rates were calculated to calibrate the PM equation. The quantile regressor was applied at various quantiles (0.1, 0.3, 0.5, 0.7, and 0.9) using the LSTM model and demonstrated acceptable accuracy with an R2 of 0.56 and root mean square error (RMSE) of 0.15 g m− 2 min− 1, which was slightly better than those of multivariate regression (R2= 0.53, RMSE = 0. 16 g m− 2 min− 1). The proposed model provided highly robust transpiration rate predictions than multivariate regression. Utilizing quantile regression with neural networks can improve water management and optimize resource use in controlled environments.