Comprehensive guide to 3D modeling of coffee plants — from architectural reconstruction and functional-structural plant models to finite element analysis for harvest optimization and light interception simulations for agroforestry systems.
Accurate model of structural elements is necessary to model the foliage and fruit distributions in cultivated plants, both of them being key parameters for yield prediction [1][4][8].
Plant architecture can be described at various degrees of detail [1][8]. The level of details in architectural data collection could vary, simplifying the data collection when plants get older and because of the high time cost required [1][4][8].
3D modeling of coffee plants serves multiple purposes [1][2][3][4][5][6][7][8][9][10]:
The coffee tree architecture, described as Roux's model, is characterized by a continuous growth and dimorphic axes [1][8].
Roux's model and architectural decomposition scales
Three scales of decomposition are used to reconstruct coffee plant architectures [1][4][8]:
Whole plant architecture and topology
Orthotropic trunk and plagiotropic branches (1st, 2nd, 3rd order)
Individual internode + leaf + axillary bud units
The coffee tree architecture follows Roux's model, characterized by [1][8]:
The number of metamers of the 2nd order axes was shown to be linearly proportional to that of the orthotropic trunk [1][4][8].
This ratio of metamer emission rhythm between the orthotropic trunk and plagiotropic axes represents the pillar of botanical events in C. arabica development and was central in modeling approaches, especially to reconstruct missing data [1][4][8].
A software integrating mathematical, computational, and statistical methods for accurate 3D coffee reconstruction [1][4][8]
Three interconnected modules ranging from user interface to input of information related to plant, field and productivity [1][4][8]:
Function: Inclusion of missing data at metamer scale
Receives MTG with mixed levels of detail and performs reconstruction of missing data [1][4][8]
Function: Geometrical correction of axes orientation and leaf pair position and orientation [1][4][8]
Function: Visualization of the 3D plant structure [1][4][8]
When coffee plants were sampled under mixed levels of details, the MTG needed to be topologically complemented by AmostraCafe3D [1][4][8].
Probabilities were generated based on properties of completely measured axes classified in the same cluster, attributing presence/absence at each node along partially measured 2nd order axes [1][4][8].
Probabilities were randomly chosen by running the 'choice' function, which uses the Mersenne twister as the core generator [1][8].
After clustering the branches by their position along the orthotropic axis (considering 40 cm-thick layers), the 2nd order axes were filled by metamer number based on [1][4][8]:
It was assumed that the second order axes have the same phyllochron with 3rd, 4th and 5th order axes. A linear model was applied, permitting synchronization to the emitted metamer number in partially measured axes [1][8].
If the leaf length and width were defined, the reproduction of individual 3D coffee leaf was constructed on 16 triangles, performed by reduction of leaf length and width equally [1][4][8].
Individual leaves are reconstructed with accurate geometry based on measured dimensions.
The number of berries per metamer was modeled as a Gaussian function within a specific zone along the plagiotropic axes [1][4][8].
Coefficient of the linear regressions between the number of metamers and the length of orthotropic and plagiotropic axes [1][8]
| Production Year | Variable | Planting Pattern & Density | |||
|---|---|---|---|---|---|
| Q10 (6000/ha) | Q6 (10,000/ha) | R10 (6000/ha) | R6 (10,000/ha) | ||
| 1st year | Metamer number | 1.124 | 1.000 | 0.871 | 0.974 |
| Length of 2nd order axes | 1.033 | 0.891 | 0.828 | 0.960 | |
| 2nd year | Metamer number | 0.959 | 1.138 | 0.895 | 0.930 |
| Length of 2nd order axes | 0.794 | 0.859 | 0.931 | 0.798 | |
| 6th year | Metamer number | 0.956 | 0.868 | 0.887 | 0.793 |
| 7th year | Metamer number | 0.905 | 0.930 | 0.894 | 0.705 |
Non-destructive coffee plant architecture coding, reconstruction and plant photosynthesis estimations using OpenAlea platform [2][10]
3D reconstructions and inclusion of parameters calculated from light response curves allowed estimation of [2][10]:
3D light interception model applied to coffee agroforestry systems with heterogeneous canopy layers [3][7]
Shade trees (9% cover) reduced aPAR in coffee by 14% annually [7]
Shade trees increased fraction of diffuse irradiance below their crown [7]
Incident PAR absorbed by soil due to inter-row spaces [7]
Computational modeling of coffee branches using scanning techniques and experimental validation through modal analysis [5][9]
where [m] = mass matrix, [c] = damping matrix, [k] = stiffness matrix
Protocol for foliage modeling and light partitioning in Coffea arabica using VPlants software [6]
Gaussian function for berry distribution along plagiotropic axes [1][4][8]
where a = maximum productivity value, b = position index, c = spread parameter
where Nb(i) = number of berries on the boundaries
where Ceil = ceiling function, Nb = total berry number, Vi = number of berries in each node
Validation: The measured and computed sum of berries of the 2nd order axes were compared using the whole dataset regardless of planting pattern (square vs rectangular) and density, showing good agreement [1][8].
Rakocevic & Androcioli: Morphophysiological characteristics of Coffea arabica in different arrangements - 3D virtual plant approach [1]
VPlants protocol for foliage modeling and light partitioning in Coffea arabica [6]
Charbonnier et al.: MAESTRA light interception modeling in coffee agroforestry systems [3][7]
Rakocevic et al.: OpenAlea functional-structural plant modeling for C. canephora photosynthesis estimation [2][10]
Three-Dimensional Scanning and Computational Simulation of Coffee Tree Branches - Finite Element Analysis [5][9]
Rakocevic M., et al. (2011). Coffee Science [1][4][8]
Three modules (AmostraCafe3D, VirtualCafe3D, Cafe3D); linear proportionality between orthotropic trunk and plagiotropic branch metamers; Gaussian berry distribution; validation across datasets.
View AbstractRakocevic M., et al. (2022). Tree Physiology 43(4):556-574 [2][10]
OpenAlea platform; 17-month Robusta vs Conilon; leaf inclination/size/allometry correlated with assimilation; Rd differences; intraspecific biomass partitioning; high potential for drought adaptation.
View Abstract(2025). Agriculture 15(13):1326 [5][9]
Finite Element Method; 3D scanning; vibration frequency error 2-20%; modal analysis validation; applications for mechanized harvesting optimization.
View AbstractCharbonnier F., et al. (2014). World Congress of Agroforestry [3][7]
14% aPAR reduction from 9% shade tree cover; 20% diffuse irradiance increase; 35% soil PAR absorption; negative exponential with shade density; prospective simulations for system design.
View AbstractRakocevic M., et al. (2013). 7th FSPM Conference [6]
Virtual coffee trees from partial data; AmostraCafe3D + VirtualCafe3D; leaf area, STAR, transmitted PAR validation.
View AbstractPeer-reviewed sources and authoritative references cited in this research
* Additional references available in the complete Publications Database. All sources are peer-reviewed.