IoT based dynamic Bayesian prediction of crop evapotranspiration in soilless cultivations

As water resources become scarce around the globe, a sustainable irrigation management is essential for increasing the crop use efficiency. Crop EvapoTranspiration (ET) is considered as one of the fundamental variables for correct irrigation management. Current ET prediction models adjust their system parameters: i) during time consuming training cycles; ii) by (destructive) leaf-area measurements; iii) only once during training for a particular meteorological scenario. These parameters are static. They cannot learn over time. On top of that, these models fail when some of the input variables are missing. To overcome these shortcomings, we follow an Internet of Things (IoT) approach to crop water use modeling and prediction for soilless cultivations. First, a stochastic crop coefficient confined to global radiation, Kr, is defined. Then, the evolution of Kr is modeled as state-space system with the daily temperature as input data, and global radiation and crop weight as measurement data. The latter data may also be missing at random. We derive a predictor for the posteriori probability of Kr and show that predicting crop ET via Kr it is immune to propagation of uncertainty over time due to erroneous weather forecasting. Our predictor is implemented in the cloud server of the IoT network. The sensor nodes, connected to a lightweight IoT client, supply our model with information in (soft) real-time. A web interface allows for visualization of the time series in real-time. To discuss Quality of Service (QoS) issues, we model the IoT network as synchronous data flow graph revealing its workflow and timings of the individual nodes. Sweet basil (Ocimum basilicum L.) is used to test the performance in four climatic environments and two different sampling methods: i) sample daily from the same environment in parallel; ii) sample daily from one environment and move on to the next in round robin fashion, emulating reuse of the same set of measurement equipment at a quarter of the costs. It turns out that in both cases, our Kr-based crop ET predictor outperforms the competing Baille technique. The performance of our IoT network is assessed by means of the QoS metrics response time, jitter, and availability.