# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
## 2 Rapid Plant Modeling System  
The proposed methodology aims to isolate plant physiognomy identifiers that are related to faster or slower plant development at different stages. There are hundreds of phenological stages of wheat growth (BBCH scale) [18], and the system separates the data for three generalized ones: (i) germination, (ii) plant formation and maturation, and (iii) grain maturation, which are general approximated stages for many plants, with the open possibility for the concept to be transferable to other species. Physiological identifiers, such as stomatal transpiration, the photosynthetic effect, night respiration, intercellular carbon dioxide concentration, evaporation, etc., are associated with physical and more accessible (i.e., measurable) identifiers, such as water and nutrient absorption, measured by multi-spectral cameras, precise growth measurements, images in different spectra in certain modes (day and night), etc. The implementation of experiments aims to be significantly accelerated by the simultaneous possibility of providing different conditions and the use of cameras and a mesh grid network of sensors with autonomous archival of data in real time, which is then a suitable starting point for determining the correlations between conditions and plant growth through advanced machine learning algorithms. The individual relevant parameters, which are measurable by sensors, and the correlation with the growth and development of the plant are autonomously archived in real time together with the given climatic conditions in which the plant is located, gradually forming a very large database suitable for determining correlation relationships using advanced machine learning algorithms. Thus obtained, accurate data about isolated and artificially created climatic conditions and consequent plant development are archived and, over time, are used to build a large data set of 6 million records collected about 5000 climate scenarios over a period of two years, which is suitable for applying algorithms in mathematical modeling and then predicting future plant developments upon the onset of climate change. The obtained mathematical models will be checked on a separate set of data, with the identification of the reliability of the estimate based on the forecasted conditions to provide a prognostic illustration of expected plant development. To significantly increase the reliability of prediction, models are classified into and reduced to three parts depending on the plant stage: (i) germination, (ii) plant formation and maturation, and (iii) grain maturation, and research is conducted on such divided data sets. The cycle of germination is approximated to 5-7 days in favorable and 17-20 days in unfavorable conditions. The cycle of each experiment, not counting the germination stage, is estimated to take an average of 10 weeks (with the preparation of the initial phase occurring with seedlings in large number of cases outside the chambers). After the appropriate period, the data is sent in batches to the data analysis center for further processing. With four separate zones and eight plants, or a possible eight partially separated zones, the aim is to implement a total of 5000 climate scenarios over 24 months, where some will be repeated several times to obtain the relevance of data repeatability. The result indicators are: 10 temperature permutation scenarios, 5 humidity permutations, 10 lighting profile permutations, etc. After the planned system operation over the two year period, a report on conducted experiments and scenarios is envisioned to conclude the improvement. The results imply the optimization of scenario selection: permutation ranges and resolutions of different climatic conditions, adaptation of growth, and development scenarios with available equipment, spatial distribution, and climate zones. Following from the above, the objectives of the system are:To develop the IoT-based apparatus for rapid collection of plant growth data, storage, and processing.