Comparison of Dynamic Tomato Growth Models for Optimal Control in Greenhouses

TL;DR

This paper compares three tomato growth models to determine their suitability for optimal control in greenhouse farming, highlighting their similarities, differences, and implementation challenges for improving agricultural efficiency.

Contribution

It provides a comparative analysis of three established tomato models, assessing their accuracy and practicality for use in optimal control within greenhouse environments.

Findings

All models predict yield with similar accuracy.

Only two models are currently suitable for optimal control.

Differences in models affect control strategies and calibration needs.

Abstract

As global demand for efficiency in agriculture rises, there is a growing interest in high-precision farming practices. Particularly greenhouses play a critical role in ensuring a year-round supply of fresh produce. In order to maximize efficiency and productivity while minimizing resource use, mathematical techniques such as optimal control have been employed. However, selecting appropriate models for optimal control requires domain expertise. This study aims to compare three established tomato models for their suitability in an optimal control framework. Results show that all three models have similar yield predictions and accuracy, but only two models are currently applicable for optimal control due to implementation limitations. The two remaining models each have advantages in terms of economic yield and computation times, but the differences in optimal control strategies suggest that they require more accurate parameter identification and calibration tailored to greenhouses.