Describing Lettuce Growth Using Morphological Features Combined with Nonlinear Models

Abstract

The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R2 (RTPCA2=0.9097, RTPCP2=0.8536) and lower MAPE (MAPETPCA=0.0284, MAPETPCP=0.0794) values, whereas the Logistic model produced a better fit for changes in PH (RPH2=0.8991, MAPEPH=0.0344). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production.

Determining Optimal Nonlinear Regression Models for Studying the Kinetics of Fatty Acid Ruminal Biohydrogenation In Vitro

“…The use of mathematical models enables accurate predictions and saves time by testing the best model with a high accuracy and low error. In most studies, nonlinear regression models are employed to examine UFA kinetics during ruminal BH. − The parameters in nonlinear models often possess a biologically meaningful interpretation, based on the units and definitions associated with them. − This interoperability renders nonlinear models valuable for gaining insights into biological processes. It should be noted that nonlinear regression models also encounter limitations.…”