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ISHS Acta Horticulturae 456: II Modelling Plant Growth, Environmental Control and Farm Management in Protected Cultivation

MODELING THE EFFECTS OF AIR TEMPERATURE PERTURBATIONS FOR CONTROL OF TOMATO PLANT DEVELOPMENT

Authors:   Brian J. Sauser, D. G. A. Giacomelli, D. H. W. Janes
Keywords:   machine vision, controlled environment, Lycopersicon esculentum
Abstract:
The primary objective of the investigation was to evaluate the effects of selected perturbations in air temperature on the development of the tomato plant, Lycopersicon esculentum (c.v. Laura, DeRuitter Seeds Laura F1, Tm-C2-V-F2). The approach to this investigation was to quantify the plants responses to air temperature and organize this information to develop an environmental control model for tomato plant growth while integrating the information with machine vision technologies. The focus was on the effect of selected air temperature perturbations on crop growth and scheduling. The objectives were accomplished through growth chamber experimentation and model development in coordination with non-destructive machine vision technologies.

Three replications were performed at three different air temperatures (high, normal, low). These experiments were used to develop baseline data for calibration of an empirical model and correlation with machine vision images. The model would allow for the quantity of biomass to be predicted at a given air temperature under constant air temperature conditions.

Results from the growth chamber studies indicated that the small air temperature differences had the effect of altering the time to first flower for the tomato plant. However, under the three different temperature regimes the dry weight of the aerial portion of the plant at time of flowering was similar for each crop, and seemingly independent of air temperature. Preliminary, results of the plant model indicated that it was capable of predicting developmental rates and changes in the tomato plant based on the dry weight of the aerial portion of the plant. The correlation of the machine vision images with dry weight can be used with the model for plant developmental predictions and development of a control system for maintaining plant scheduling.

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