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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. paliurus grows faster than other plants, increasing the demand for propagation through leaf and stem cuttings to produce seedlings. However, this species requires pre-control of environmental factors such as high temperatures (25–30 °C), humidity (80–90%), and specific light (2000 to 3000 lux) intensity levels during the cutting and seedling production process. However, it is difficult to predict suitable environments for the growth of C. paliurus. This study requires the use of big data technology to parameterize the method of intelligent control of the environment used in the process of making stakes and creating seedlings. Our main results were that an improved convolutional neural network and short long-term memory (LSTM) in big data technology were used with a new method, multipath hole convolution (MPCNN), to predict environmental factors in production of seedlings. Also, the research results show that the MPCNN and LSTM methods can accurately predict the necessary temperature, humidity, and light conditions in the production process of C. paliurus seedlings. For the prediction of environmental characteristics related to this species, the light characteristics have a high error distribution, but the method described here was able to accurately control this variation, with an error of less than 2%.
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. paliurus grows faster than other plants, increasing the demand for propagation through leaf and stem cuttings to produce seedlings. However, this species requires pre-control of environmental factors such as high temperatures (25–30 °C), humidity (80–90%), and specific light (2000 to 3000 lux) intensity levels during the cutting and seedling production process. However, it is difficult to predict suitable environments for the growth of C. paliurus. This study requires the use of big data technology to parameterize the method of intelligent control of the environment used in the process of making stakes and creating seedlings. Our main results were that an improved convolutional neural network and short long-term memory (LSTM) in big data technology were used with a new method, multipath hole convolution (MPCNN), to predict environmental factors in production of seedlings. Also, the research results show that the MPCNN and LSTM methods can accurately predict the necessary temperature, humidity, and light conditions in the production process of C. paliurus seedlings. For the prediction of environmental characteristics related to this species, the light characteristics have a high error distribution, but the method described here was able to accurately control this variation, with an error of less than 2%.
Technical Scheme for Cutting Seedlings of Cyclocarya paliurus under Intelligent Control of Environmental Factors
Wanxia Yang (author) / Jiaqi Zhuang (author) / Yuan Tian (author) / Shiying Wan (author) / Siyu Ding (author) / Mei Zhang (author) / Shengzuo Fang (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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