# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
###### Abstract  
Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravative factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest. encapsulated design plant growth chambers; rapid modeling of plant development; IoT; artificial intelligence; predictive agriculture +
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areas of modern agriculture [11]. Moreover, climate change is one of the most expressed aggravating factors for obtaining the relevant datasets. Existing datasets today are obtained from drones or satellites covering whole fields rather than individual plants and via observing the conditions using various spectra [12]. The most precise kinds of datasets are gained from the perspectives of microbiological identifiers, which imply scarce numbers of often invasive samples that are periodically analyzed with specialized equipment and expensive devices (in the range of EUR 100,000) [13]. The IoT approach implies cheap sensors (in range of EUR 10-100) while the low measurement quality is compensated for by the data size [14]. However, these cheap sensors mostly monitor weather and soil conditions, while the development of sensors capable of monitoring plant status is an ongoing field of research, with examples existing that can capture electrophysiological measurements [15] or plant acoustic emissions [16]. The issue of the slow collection of large amounts of relevant historical data is tackled here by creating specially designed bioreactors that serve as rapid plant-model identification systems for multiple simultaneous climate zones, supported by autonomous real-time data acquisition and archiving. Instead of the usual observed annual life cycles in nature, the system introduces equipment that can be used for the rapid, simultaneous implementation of a number of different experiments in a climate-encapsulated system with control loops of light, temperature, humidity, pH, and nutrient profiles, with an extensive network of sensors and with the help of multi-spectral cameras. The equipment is supported by software in the form of the autonomous storage of identifiers in a central database. For the exemplary case of wheat crops, a single encapsulated design enables 8-12 simultaneous plant groups, each one as an individual field emulation capable of squeezing up to two yield cycles in a single year, resulting in a total of 16-24 harvests per year per device; each device is the size of a computer server cabinet. With four available devices, the number rises almost up to 100 harvests per year.