Most home fruit growers acquire the necessary knowledge on fruit tree care from horticultural handbooks in combination with tree training courses. The learning process is therefore slow and meanwhile trees are often inadequately cared for. Although many kinds of fruit trees can be found in these gardens, the most common are apple trees of various cultivars and rootstocks, most of which are free standing and trained in the spindle form (Tojnko et al., 2011). Pruning has a direct influence on tree form development, growth intensity, time to yield, the balance between growth and fruiting, and renovation of bearing wood and, along with bending, is necessary in both intensive fruit production and free-standing tree forms (Lakso and Corelli Grappadelli, 1991; Unuk et al., 2008; Wünsche et al., 2000).
To make the learning process faster and easier, the interactive education application EduAPPLE (Kohek et al., 2015) has been developed, where tree-training techniques can be applied on three-dimensional (3D) apple tree models. The user can see the tree from different directions and freely rotate it while performing the desired operations. The application then simulates tree response to these actions. It allows 3D simulation of tree training toward the desired shape, which saves costs and allows training in regions (e.g., cities) where travel to an orchard is not practical. It is also ideal for introducing students to the effects of horticultural manipulation on apple trees.
The application is based on the new growth model, originating in the work of Pałubicki et al. (2009), where tree growth is defined by the reaction of buds to available resources (light, water, nutrients, and assimilates). Pruning and branch positioning change the growing conditions by redistributing resources to the remaining branches. Therefore, our approach to tree light interception and growth resources redistribution enables fast, realistic, and autonomous responses to pruning and branch positioning. The consequence is a user-independent pruning and bending response true to that documented in the literature. The results become visible in the next growing season when new shapes are formed as trees respond to the realized actions.
Since potential users apply various cultivars and rootstocks and differ in their use of plant protection products, we used the same approach as that already found in tree pruning handbooks and show only the most common tree responses in the form of changes in tree crown structure without predicting fruit load or quality. An average user is not expected to have the large amount of information necessary for the crop amount prediction (Lauri and Lespinasse, 2001; Lauri et al., 1996, 2011; Wünsche et al., 2000). Therefore, our aim is to provide a general understanding of tree training techniques and their influence on tree light interception and thus the yield potential for fruit crops (Stephan et al., 2008).
For better evaluation of training actions, tree light interception and light distribution through the canopy can be examined by the use of implemented visualization. To perform all the necessary operations in real-time, a graphics-processing unit is used to complete the most complex parts of the computation.
The first virtual reality system for growth following the pruning of apple trees was based on apple trees digitized from orthogonal photographs (Atkins et al., 1996). Like the majority of early pruning simulators, the Atkins’ simulator was driven by a rule-based model, with predefined pruning responses without consideration of actual shading effects.
Because of their rigidity, rule-based models were later replaced by functional-structural plant models. SIMWAL was one of the first structural-functional tree models developed for training walnut trees [Juglans regia (Balandier et al., 2000)]. It simulated the 3D structural dynamics of a tree and biomass partitioning among its internodes, buds, leaves, and roots over a period ranging from a few months to several years according to climatic conditions [temperature, radiation, and air carbon dioxide (CO2) concentration] and pruning. Their approach was quite accurate but slow, thus preventing its usage in real-time applications.
L-PEACH is an L-system (Lindenmayer, 1968; Prusinkiewicz and Lindenmayer, 1990) based functional-structural probabilistic model that helps to understand peach tree (Prunus persica) growth and fruiting (Lescourret et al., 1998; Lopez et al., 2010; Smith et al., 2008). This model integrates important concepts related to carbon assimilation and distribution. It includes the modeling of responses to tree pruning and fruit thinning. 3D depictions of simulated tree growth are displayed on a computer screen and the user can easily interact with the model.
Another L-system based simulator, MAppleT, was introduced by Costes et al. (2008) to simulate the apple tree development in interaction with gravity. The authors combined stochastic models to simulate the tree topology and the mechanistic model for its geometry.
Lang and Lang (2008) created VCHERRY—a sweet cherry tree (Prunus avium) growth model for testing training decisions by predicting resulting fruit amounts. Fruit amount is also influenced by different selections of rootstocks, soil types, and regional climates, as predefined in the application.
A computer model for pruning practices regarding apple trees using a Hidden Semi-Markov Chain was described by Xia et al. (2009). This system can simulate several growth situations with direct feedback from different levels of pruning schemes. Environmental effects such as light reception were not considered in their work, limiting the simulation of larger trees, where self-shading becomes an important factor for tree growth.
A competition-based model of pruning apple trees based on L-systems within the powerful OpenAlea framework was introduced by Cokelaer et al. (2010) and Pradal et al. (2008). As only basic physiological processes are included, it is possible to estimate the parameters to fit the growth of control trees and to reproduce a realistic relationship to pruning. The use of their framework is quite complex, making their tool impractical for educational purposes.
More advanced functional–structural tree growth models consider significantly more factors than those that are self-organizing but are slower and therefore also inappropriate as an interactive education tool. In contrast, our model is fast and fully autonomous as it works in a self-organizing way. Therefore, it can be applied to trees of any age and to branches at any position or orientation.
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