Evaluation of the Reduced State-Variable TOMGRO Model using Boundary Data

Shamshiri, Ramin; Ahmad, Desa; Zakaria, AbdJamil; Ismail, Wan Ishak Wan; Man, Hasfalina Che; Yamin, Muhammad

doi:10.13031/aim.20162454205

Cited by 1 publication

(2 citation statements)

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“…The objective was to provide a standalone application in a way that end users unfamiliar with programming languages and/or crop modeling would have an easier access to yield prediction in different greenhouse environments. Data from spreadsheet version of the model were used for testing the Simulink blocks and validation of the results [25]. The five state variables included in the tomato growth model of Ref.…”

Section: Simulation Of Expected Yieldmentioning

confidence: 99%

“…This description completely explicates the reduced state-variable tomato model implemented in Simulink for this project, and the state-variable equations for LAI, total biomass accumulation (dW=dt), total fruit weight (dW F =dt), and mature fruit weight ((dW M =dt)) are highlighted. The implemented model was validated [25] using the Lake City experiment datasets of Ref. [24] to show that the Simulink version of the model is an exact replication of the original spreadsheet version.…”

Section: Plant Engineeringmentioning

confidence: 99%

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Adaptive Management Framework for Evaluating and Adjusting Microclimate Parameters in Tropical Greenhouse Crop Production Systems

Shamshiri¹,

Mahadi²,

Thorp³

et al. 2017

Plant Engineering

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High operational costs of greenhouse production in hot and humid climate condition due to the initial investments on structure, equipment, and energy necessitate practicing advanced techniques for more efficient use of available resources. This chapter describes design and concepts of an adaptive management framework for evaluating and adjusting optimality degrees and comfort ratios of microclimate parameters, as well as predicting the expected yield in greenhouse cultivation of tomato. A systematic approach is presented for automatic data collection and processing with the objective to produce knowledge-based information in achieving optimum microclimate for high-quality and high-yield tomato. Applications of relevant computer models are demonstrated through case-study examples for use in an iterative way to simulate and compare different scenarios. The presented framework can contribute to future studies for providing best management decisions such as site selection, optimum growing season, scheduling efficiencies, energy management with different climate control systems, and risk assessments associated with each task.

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Section: Simulation Of Expected Yieldmentioning

confidence: 99%

Section: Plant Engineeringmentioning

confidence: 99%