Technical Scheme for Cutting Seedlings of Cyclocarya paliurus under Intelligent Control of Environmental Factors

Cyclocarya paliurus is a species with high economic, horticultural, and medicinal value. C. grows faster than other plants, increasing the demand for propagation through leaf stem cuttings to produce seedlings. However, this requires pre-control of environmental factors such as temperatures (25–30 °C), humidity (80–90%), specific light (2000 3000 lux) intensity levels during cutting seedling production process. it difficult predict suitable environments growth paliurus. This study use big data technology parameterize method intelligent control environment used in process making stakes creating Our main results were that an improved convolutional neural network short long-term memory (LSTM) new method, multipath hole convolution (MPCNN), Also, research show MPCNN LSTM methods can accurately necessary temperature, humidity, conditions For prediction characteristics related species, have error distribution, but described here was able variation, less 2%.