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

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.