Long-term prediction of biosignals from greenhouse-grown tomato

Artificial neural networks (ANN) have been successfully used for modelling climate and plant signals in greenhouses. This work shows the combination of two ANN models, built and trained separately, and then coupled together. Both models were trained using a data set consisting of 3 complete cultivation periods (2011 until 2013) of 2 venlo-type greenhouses located at the Humboldt-Universität zu Berlin. The greenhouses were used for soilless cultivation of tomato. The first model considered (climate prediction: CP) estimated the expected values of temperature and relative humidity in the near future (one-step prediction, OSP). The output was then recursively fed to the model in order to make further predictions. Each time step represented 5 min, and the maximum number of prediction steps was set to 6. On the other hand, the output of the second model (plant signals: PM) was not a prediction in time, but an estimation of plant signals (leaf temperature, transpiration rate, photosynthesis rate) as expected to be measured by a phytomonitoring system (BERMONIS). The combined models took the 6 predicted steps of climate (long-term prediction: LTP) produced by CM and used them to feed PM, thus generating a Biosignals LTP. As expected, the fit of the LTP decreased with the number of forward steps. For example, the R² for leaf temperature range from 0.946 for the first simulation until 0.704 for the 6^th simulated step. For transpiration and photosynthesis rate, these ranges were (0.874, 0.809) and (0.772, 0.692), respectively. These results show that the estimated plant responses could support a predictive control system, in order to avoid plant damage due to extreme climate conditions.