Precise control and prediction of the greenhouse growth environment of Dendrobium candidum

Objective: Dendrobium officinale is a perennial epiphytic herb of the Orchidaceae family. It has very strict needs with respect to growth environment and climatic conditions. In order to precisely control the artificial growth environment of Dendrobium candidum, a monitoring system was designed and implemented using a Programmable Logic Controller (PLC) and Supervisory Control and Data Acquisition (SCADA) system. Methods: A platform was created for precise control using a Siemens S7-200CPU224 PLC and different sensors of various environmental parameters as the control unit and SCADA-configured software as the core of the monitoring unit. We here used 4 indexes, soil temperature, soil moisture, humidity, and light, as prediction parameters and established a three-layer feed-forward fuzzy optimization neural network model. Results: The system not only allows prediction of the optimal environmental parameters for the growth of Dendrobium candidum, real-time monitoring, and intelligent control but also escapes the shortcomings of traditional back-propagation (BP) neural networks, which suffer from slow convergence, shock, and poor generalization. The current model's average prediction error is less than 2.5%. It also provides a theoretical basis and decision support for the precision control of planting projects and relevant environment forecasting. The climate in the test area is hot and rainy in summer and colder and drier in winter. The annual precipitation is concentrated in spring and summer, peaking twice, in May and October. The subtropical high temperature was recorded in August, which has little rainfall and is prone to drought. Winter features both cold and warm air and some rainy days, but not as much overall precipitation as summer.