Article

Long time plant response measurements for yield prediction, water use and climate control optimization using gas exchange measurements in semi closed and ventilated greenhouses

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Abstract

An advanced prototype of a phytomonitor was developed at Humboldt University in the frame of the national ZINEG project for low energy greenhouses (www.zineg.de). Ten leaf cuvettes were allocated to different tomato leaves in the canopy to get a representative average of the gas exchange of younger and older leaves under shaded and non-shaded conditions. The cuvettes were constantly attached to several plants for the whole cultivation period, with a seven-day interchange period. Two instruments were used in two greenhouses with different climate control systems (semi-closed and a ventilated greenhouse as reference) to show the differences in the climate – canopy interaction. With the help of the Mollier Plot Analyzer software, developed at Humboldt University, the climate comfort zone of the canopy for maximum photosynthetic light use efficiency was found. With the calculation of the accumulated CO2 and transpired water, the differences in the yield expectation for the next four weeks and the plant consumed water was estimated. From the result of the light use efficiency evaluation with the Mollier Plot Analyzer, the comfort zone for tomato growing in the semi-closed greenhouse was estimated to be in a temperature range from 20 to 28°C, with a relative humidity of 75 to 95%. In the ventilated greenhouse most of the condition points with lower light use efficiency were found to have higher temperature and lower relative humidity. The difference in the yield between the semi-closed and ventilated greenhouse was shown by the difference in CO2 uptake measured by the phytomonitors and the difference in the photosynthetic light use efficiency of the canopy in both greenhouses. The calculated water consumption using the gas exchange measurement data showed a high correlation to the measured water consumption. With this result it should be possible to apply gas exchange measurement systems not only for climate control but also for irrigation control.

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... Kläring and Krumbein (2013)). Methods that mechanically attach the measurement device to the leaves are better suited for autmated climate control, although they are expensive and need to be relocated as the plants grow (Schmidt, 1992;Schmidt et al., 2014). ...
Thesis
Moderne Präzisionsgartenbaulicheproduktion schließt hoch technifizierte Gewächshäuser, deren Einsatz in großem Maße von der Qualität der Sensorik- und Regelungstechnik abhängt, mit ein. Zu den Regelungsstrategien gehören unter anderem Methoden der Künstlichen Intelligenz, wie z.B. Künstliche Neuronale Netze (KNN, aus dem Englischen). Die vorliegende Arbeit befasst sich mit der Eignung KNN-basierter Modelle als Bauelemente von Klimaregelungstrategien in Gewächshäusern. Es werden zwei Modelle vorgestellt: Ein Modell zur kurzzeitigen Voraussage des Gewächshausklimas (Lufttemperatur und relative Feuchtigkeit, in Minuten-Zeiträumen), und Modell zur Einschätzung von phytometrischen Signalen (Blatttemperatur, Transpirationsrate und Photosyntheserate). Eine Datenbank, die drei Kulturjahre umfasste (Kultur: Tomato), wurde zur Modellbildung bzw. -test benutzt. Es wurde festgestellt, dass die ANN-basierte Modelle sehr stark auf die Auswahl der Metaparameter und Netzarchitektur reagieren, und dass sie auch mit derselben Architektur verschiedene Kalkulationsergebnisse liefern können. Nichtsdestotrotz, hat sich diese Art von Modellen als geeignet zur Einschätzung komplexer Pflanzensignalen sowie zur Mikroklimavoraussage erwiesen. Zwei zusätzliche Möglichkeiten zur Erstellung von komplexen Simulationen sind in der Arbeit enthalten, und zwar zur Klimavoraussage in längerer Perioden und zur Voraussage der Photosyntheserate. Die Arbeit kommt zum Ergebnis, dass die Verwendung von KNN-Modellen für neue Gewächshaussteuerungstrategien geeignet ist, da sie robust sind und mit der Systemskomplexität gut zurechtkommen. Allerdings muss beachtet werden, dass Probleme und Schwierigkeiten auftreten können. Diese Arbeit weist auf die Relevanz der Netzarchitektur, die erforderlichen großen Datenmengen zur Modellbildung und Probleme mit verschiedenen Zeitkonstanten im Gewächshaus hin.
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