Application of bottom-up virtual reality modelling to optimization of behaviour of commercial plants.

Traditional experimental methods in plant vegetative
propagation are expensive, time-consuming and without good
practical results. Creation of an analogue computer model is
proposed for better understanding of complex plant behaviour

Although the process of plant rooting and growth has been extensively studied over a number of years, it still remains one of the few physiological processes that is not well understood. Yet understanding this process is essential for the optimization of plant behaviour and improving commercial results in vegetative multiplication of plants. To facilitate more conclusive experimental results, better experimental tools are needed, such as computer simulation tools. However, the state-of-the-art of these models is primarily based on a top-down approach, where the plant is modelled as a whole, using systems of equations. As the complexity of the equations is proportional to the complexity of the modelled system, such models give unreliable results, and are difficult and expensive to develop, validate and run. Simplified top-down models have been developed as a compromise, dealing with a limited number of parameters, but the usability of such models is limited due to a fundamental limitation of top-down models to model only what is known to the model developer. This project focuses on the development of a new, bottom-up computer model of plants using complexity theory principles. The models will be based on complex interaction of plant components, giving rise to model behaviour that is analogous to that of the real plant, thus comprising a better experimental tool for rapid investigation and optimization of plant behaviour. The new model will be validated for olive, walnut and eucalyptus, and some of the major species within difficult to root varieties. These are with worldwide potential and propagated by vegetative multiplication procedures. The Problem: Plant nursery companies want results that are pragmatic and reduce time-to-market in vegetative propagated crops. However, extensive experiments of plant behaviour in difficult to root varieties have always been inconclusive, and created a puzzle for scientists in terms of variability to equal inputs or absence of results, leading to more sophisticated experiments for investigation of biochemical and molecular process interactions, with equally inconclusive practical results. There are several difficulties associated with experimental work in vegetative plant propagation: Available periods during the year when the trials can be performed are limited to once or twice a year. Experiments are time consuming, requiring expensive facilities, such as specialised greenhouses. Considerable human resources are required to collect material and treat it. Skilled staff are required to supervise the final measurements and even more sophisticated staff and facilities are needed if other types of analysis are to be done (measurements of biochemical compounds in plant or histological periodic cuts, among others). State-of-the-art and the information available about external inputs that may induce or benefit plant behaviour are not always applicable to the material used in the experiments, due to different genetic background and the different provenance of vegetal matter. The Solution: The above practical problems make a case for use of an efficient computer model that would replace experimentation on real plants with experimentation on virtual plants. Experimentation with such models would not be limited by the time of the year, and would require a considerably smaller amount of facilities and resources. However, state-of-the-art computer models of plants are either geometry-based, such as L-systems, or process-based, such as biologically inspired models. A fundamental disadvantage of all these models is their top-down approach. This approach creates a global model of the plant, using either axioms and recursive production rules (L-systems), or differential equations (process-based systems). Plants are, however, complex systems, and the top-down approach requires a proportionally complex number of rules or equations to be defined by the model engineer. This very quickly becomes an impossible task, in a similar way as attempting to use a large number of equations to model a flock of birds. The proposed project will adopt a radically different bottom-up approach as a more appropriate method for modelling of complex systems. The aim of the project will be: 1) to develop a process, model and software that will enable the creation of an analogue and dynamic model-of plant growth in virtual reality, by incorporating biochemical and physical inputs into each of the model components. These elementary components envisaged are leaf, stem and root. New copies of these components will be created as the plant grows, and they will grow in volume on the basis of available growth resources, such as carbohydrates, nutrients and water. The interaction of the components will be achieved through the flow of the resources through them, and the growth by the availability of a combination of the resources together with physical, chemical and biological rules. The interaction of the plant components will give rise to an emergent, or bottom- up model which will achieve far more complex behaviour than state-of-the-art top-down models without the need for the model designer to define the complexity of the system model. The plant models will be created according to the main characteristics of olive, eucalyptus and walnut species. 2) to simulate the root behaviour in scions from difficult to root varieties of olive, eucalyptus and walnut species, using the bottom-up approach and the model developed in 1), on the basis of external inputs. The model will be calibrated using data from detailed instrumental monitoring of real plants. Soil and environmental parameters will be monitored, as well as the properties of the plant. The calibration will involve the data from a sufficient number of cases to enable the model to reliably predict the plant growth and behaviour. Potential benefits from this model are: * reduced time and cost of experiments * experiments can be performed regardless of the season * reduced need for available material * the ability to investigate the influence of a number of factors, both individually and simultaneously * the potential to give immediate solutions to specific practical problems * the potential for a better understanding of plant behaviour * the potential for more conclusive results of the experiments * better control of harvest date to address market demand. Keywords: plant growth, bottom-up modelling, biological inputs.
Project ID: 
2 544
Start date: 
Project Duration: 
Project costs: 
790 000.00€
Technological Area: 
Market Area: 

Raising the productivity and competitiveness of European businesses through technology. Boosting national economies on the international market, and strengthening the basis for sustainable prosperity and employment.