Averaging Techniques in Processing the High Time-resolution Photosynthesis Data of Cherry Tomato Plants for Model Development

Romdhonah, Yayu; Fujiuchi, Naomichi; Shimomoto, Kota; Takahashi, Noriko; Nishina, Hiroshige; Takayama, Kotaro

doi:10.2525/ecb.59.107

Cited by 6 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1). This inexpensive system for monitoring photosynthesis and transpiration has since been installed on tomato plants in commercial greenhouses, and then the net photosynthetic and transpiration rates measured at 5-min interval have been used in several studies (Romdhonah et al, 2021a(Romdhonah et al, , 2021bFujiuchi et al, 2022). Machine learning methods have been used to develop yield prediction models for open-field and greenhouse crops in recent years (Ehret et al, 2011;De Alwis et al, 2019;Alhnaity et al, 2020;van Klompenburg et al, 2020;Dharani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Using a Real-Time Photosynthesis and Transpiration Monitoring System to Develop Random Forests Models for Predicting Cherry Tomato Yield in a Commercial Greenhouse

FUJIUCHI,

INABA,

et al. 2024

Environmental Control in Biology

Self Cite

View full text Add to dashboard Cite

Predicting the yield of horticultural crops is crucial to meet the expectations of retailers and consumers. In this study, we developed random forests (RF) based on the measured amounts of whole-plant photosynthesis and transpiration to predict cherry tomato fruit yields in a commercial greenhouse in Japan. Whole-plant daily net photosynthesis (Photo) and daily transpiration (Trans) were measured by using a real-time photosynthesis and transpiration monitoring system. Variables of environmental conditions (Env), including daily solar irradiation, air temperature, and atmospheric water vapor deficit, were also measured in the greenhouse. Data with different 7 variable combinations (Env, Photo, Trans, EnvϩPhoto, EnvϩTrans, PhotoϩTrans, EnvϩPhotoϩTrans) and different 21 timeframes (from 1 to 6 consecutive weeks in the past 6 weeks) were used to train models for predicting the yield for the subsequent week. RF models with the timeframes of 3 consecutive weeks until 2 weeks before the date of yield prediction (3W2) and 4W2 and variable combinations of Photo, EnvϩPhoto, and PhotoϩTrans had relatively low normalized root mean square error (RMSE%; 9.8-10.3%). The model that had a timeframe 4W2 and variable combination Photo had the best accuracy (RMSE% ϭ 9.8%). These indicate that whole-plant photosynthesis and transpiration are good predictors of cherry tomato yield.

show abstract

Section: Introductionmentioning

confidence: 99%

Using a Real-Time Photosynthesis and Transpiration Monitoring System to Develop Random Forests Models for Predicting Cherry Tomato Yield in a Commercial Greenhouse

FUJIUCHI,

INABA,

et al. 2024

Environmental Control in Biology

Self Cite

View full text Add to dashboard Cite

show abstract

Co2 Distribution Under Co2 Enrichment Using Cfd Considering Photosynthesis Model in a Tomato Greenhouse

Nurmalisa¹,

Tokairin²,

Kumazaki³

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

Comparison of photosynthetic rates, transpiration rates, and total conductance of greenhouse-grown tomato plants measured with two open chambers with different ventilation rates

Shimomoto

Fujiuchi

Takahashi

et al. 2021

J. Agric. Meteorol.

Self Cite

View full text Add to dashboard Cite

A real-time monitoring system was developed and applied to monitor the time course of photosynthesis and transpiration in fully-grown tomato plants in a semi-commercial greenhouse. The system was based on an open chamber method in which the ventilator airflow rate is an important parameter affecting the environmental factors in the chamber and physiological response of plants enclosed inside the chamber. So, we assumed that the effects of this parameter on these responses should be evaluated for an agricultural production site. In this study, we investigated differences in the environmental factors in the chamber and physiological response of whole-tomato plants obtained from two chambers) implemented with a single ventilator (SV, 0.36 m 3 min -1) or double ventilators (DV, 0.72 m 3 min -1 ). The relative humidity and vapor pressure deficit inside the SV chamber were about 10% higher and 0.5 kPa lower than those inside the DV chamber because of the difference in air exchange rates. However, we found no significant effect of airflow rate on net photosynthetic rate, transpiration rate and total conductance of the plants in the SV and DV chambers by analyzing with weighted Deming regression. This simultaneous monitoring method, undertaken in multiple chambers, and weighted Deming regression analysis can be used to check whether measurement conditions are appropriate for on-site monitoring.

show abstract

Averaging Techniques in Processing the High Time-resolution Photosynthesis Data of Cherry Tomato Plants for Model Development

Cited by 6 publications

References 11 publications

Using a Real-Time Photosynthesis and Transpiration Monitoring System to Develop Random Forests Models for Predicting Cherry Tomato Yield in a Commercial Greenhouse

Using a Real-Time Photosynthesis and Transpiration Monitoring System to Develop Random Forests Models for Predicting Cherry Tomato Yield in a Commercial Greenhouse

Co2 Distribution Under Co2 Enrichment Using Cfd Considering Photosynthesis Model in a Tomato Greenhouse

Comparison of photosynthetic rates, transpiration rates, and total conductance of greenhouse-grown tomato plants measured with two open chambers with different ventilation rates

Contact Info

Product

Resources

About