Using Sigmoid Growth Models to Simulate Greenhouse Tomato Growth and Development

Abstract

Mathematical modeling has been used to describe the characteristics of crop growth. Establishing a growth model can help to better understand the responses of crops to their environment and improve the efficiency of agricultural production. This study establishes empirical growth models to predict the growth of greenhouse tomato. In this study, we collected beef tomato (Solanum lycopersicum cv. ‘993′) growth data over two crop seasons in Taiwan and established growth models by employing the commonly used Gompertz and Logistic curves. Days after transplanting (DAT) and growing degree-days (GDD) were introduced as independent variables and their relationships with five traits, i.e., plant height, leaf area index, stem dry matter, leaves dry matter, and fruits dry matter were determined. The performances of GDD models were slightly better than those of the DAT models. In addition, we inferred five critical points with biological meaning based on the proposed growth models. The critical points estimated by the Logistic model are closer to our expectation than those of the Gompertz model, and they were applicable for the ‘993′ tomato in Taiwan. These results can be used to predict tomato growth and adjust the fieldwork schedule to improve the efficiency of the greenhouse production of tomatoes.

Effects of Different Salinity Levels in Drip Irrigation with Brackish Water on Soil Water-Salt Transport and Yield of Protected Tomato (Solanum lycopersicum)

“…The overall growth process of crops shows a trend of "gradual growth-rapid growthslow growth", forming an "S"-shaped curve [30], which is supported by the logistic model [19]. Therefore, this model was used to quantitatively analyze the dynamic development of plant height and stem diameter of protected tomatoes under different irrigation water salinities.…”

“…Furthermore, the fully correlated model provided a good fit for the growth patterns of all four crops. Fang et al [19] used the commonly used Gompertz and logistic curves to establish growth models, taking the days after transplanting and cumulative growing degree days as independent variables to simulate the correlation between these variables and five growth indices. The results indicated that the logistic model estimated critical points were closer to the expected values than the Gompertz model and were more suitable for tomato crops in Taiwan.…”

Combining Recurrent Neural Network and Sigmoid Growth Models for Short-Term Temperature Forecasting and Tomato Growth Prediction in a Plastic Greenhouse

“…For establishing the growth model, GDD (Equation ( 17)) [27] was used as the input variable; LAI, fresh fruit weight, and aboveground dry matter were utilized as the output variables. Two sigmoid models, namely the Logistic (Equation ( 18)) [35] and Gompertz (Equation ( 19)) [36], were used to fit the growth data and both require three parameters to characterize tomato growth:…”

“…The main advantages of the Logistic and Gompertz models lie in their simplicity and interpretability. These models frequently describe plant height, biomass accumulation, and leaf area index (LAI) as a function of time or GDD [27][28][29]. The simulation of crop growth can provide useful information for adjusting management practices and schedules [27,30].…”

“…These models frequently describe plant height, biomass accumulation, and leaf area index (LAI) as a function of time or GDD [27][28][29]. The simulation of crop growth can provide useful information for adjusting management practices and schedules [27,30]. A modern greenhouse system can monitor and control the microclimate using a wide range of sensors and equipment.…”