# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
## 3 Encapsulated Design Plant Growth Devices
### Measured Identifiers  
Physiological identifiers such as stomatal transpiration, photosynthetic effect, night respiration, intercellular carbon dioxide concentration, evaporation, etc., are associated indirectly with physical and more accessible measurable vegetation indices, which rely mostly on multi-spectral cameras as sensors. This is necessary to enable a large number of measurements, as accurate plant status identifiers from the domain of molecular biology are both timely and costly and possibly contain a correlation delay with respect to other input-output data. In order to capture both the spectral bands required for basic vegetation indices, as well as additional bands to power further analytics, a RedEdge-MX multi-spectral sensor [22] was chosen (see Figure 5).  
Figure 4: Microclimate variables in the four zones for an exemplary case study.  
Plant physiology is observed in various growth stages: BBCH classification, stem segment length, and leaf area. The vegetation indices are measured with a high resolution stereo camera in correlation with the central tower rotation angles. Details about camera image analysis in such applications can be found in [23]. For wheat culture in particular, emphasis is placed on the tillering phase (BBCH 20-30), as the stalk and future kernels already form in this stage, which is depicted as a stress-sensitive period. Therefore, the microclimate conditions' permutations are imposed here with an enhanced resolution. A multi-spectral camera was used to detect the water content of the plants with near-infrared bands (possible diseases were shown by a red-edged band and photosynthesis level by a green band), which were pre-calibrated with a CO\({}_{2}\) sensor (measuring the CO\({}_{2}\) assimilation).  
A list of all the calculated plant development indicators is provided in Table 2 along with the expected range of each indicator.  
The choice of the vegetation indices listed in Table 2 is a result of the interdisciplinary collaboration between the modeling, robotics, and agriculture experts, made after meetings and field visits with experts from the wheat cultivation domain and an extensive literature overview [24, 25, 26]. This constitutes ongoing research, and additional correlations and corresponding identifiers are expected to be confirmed during the data processing stage following the collection of the dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Calculated Vegetation Index** & **Expected Range** \\ \hline Stem length (in m) & 0 to 0.6 \\ Plant biomass (in g/m\({}^{2}\)) & 0 to 50 \\ Leaf area index & 0 to 5 \\ Normalized Difference Vegetation Index (NDVI) & \(-1\) to 1 \\ Green Normalized Difference Vegetation Index (GNDVI) & \(-1\) to 1 \\ Ratio Vegetation Index (RVI) & 0 to 25 \\ Simple Ratio (SR) & 0 to 20 \\ Photochemical Reflectance Index (PRI) & \(-0.5\) to 0.5 \\ Chlorophyll Index (CI) & 0 to 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of calculated vegetation indices.  
Figure 5: Spectral resolution of RedEdge-MX multi-spectral sensor datasheet [22].