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Cucumber greenhouse yield intelligent prediction device based on recurrent neural network

A recursive neural network and prediction device technology, which is applied in the field of intelligent prediction devices for cucumber greenhouse yield, can solve problems such as poor development of female flowers and flower organs, decrease in parthenocarpic setting rate, etc., and achieves good generalization ability, fast learning speed, and local approximation. powerful effect

Active Publication Date: 2019-08-23
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

The experimental results showed that under high temperature stress, the number of melted melons and deformed melons of European type cucumbers changed significantly. This may be due to the dysplasia of female flowers and the formation of small female flowers, which turned yellow and withered at the bud stage, unisexual Decreased seed set rate

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  • Cucumber greenhouse yield intelligent prediction device based on recurrent neural network
  • Cucumber greenhouse yield intelligent prediction device based on recurrent neural network
  • Cucumber greenhouse yield intelligent prediction device based on recurrent neural network

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Embodiment Construction

[0029] combined with Figure 1-5 , further describe the technical solution of the present invention:

[0030] 1. Design of the overall function of the system

[0031] The present invention designs an intelligent prediction device for cucumber greenhouse output, which can detect and predict the output of cucumber greenhouse soil moisture, soil temperature, ambient temperature and ambient light intensity parameters. The system consists of a cucumber based on wireless sensor network The greenhouse parameter detection platform and the greenhouse cucumber production intelligent prediction system are composed of two parts. The cucumber greenhouse parameter detection platform based on the wireless sensor network includes the detection node 1 and the field monitoring terminal 2, which are constructed into a wireless measurement and control network in a self-organizing manner to realize the wireless communication between the detection node 1 and the field monitoring terminal 2; the de...

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Abstract

The invention discloses a cucumber greenhouse yield intelligent prediction device based on a recurrent neural network. The device is characterized in that the prediction device realizes real-time detection of environmental parameters, soil parameters and cucumber greenhouse yield information of a cucumber greenhouse, and the device comprises a cucumber greenhouse parameter detection platform basedon a wireless sensor network and a greenhouse cucumber yield intelligent prediction system; according to the intelligent prediction device for the cucumber greenhouse yield based on the recurrent neural network, the intelligent early warning system detects environmental parameters, soil parameters and cucumber greenhouse yield information of a cucumber greenhouse in real time, and therefore production management of the cucumber greenhouse can be well done, and economic benefits can be improved.

Description

technical field [0001] The invention relates to the technical field of agricultural greenhouse automation equipment, in particular to an intelligent forecasting device for cucumber greenhouse output based on a recursive neural network. Background technique [0002] Cucumber is one of the main cultivated vegetable varieties in my country, and it is a temperature-loving plant. The biggest obstacle to cucumber production is low temperature and chilling injury, especially in cold years, the critical low temperature of about 15 °C during the day and between 4-8 °C at night often occurs in solar greenhouse cultivation, and has become an important adversity stress factor affecting cucumber yield. However, due to the relatively simple structure of greenhouses in my country, low temperature is still the main limiting factor affecting the growth, yield and quality of cucumbers in protected areas during the severe winter and spring seasons. Several common factors affecting the yield o...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q10/04G06Q50/02G08B21/18H04L12/40G01D21/02G05D27/02
CPCG06Q10/04G06Q50/02G08B21/18H04L12/40G01D21/02G05D27/02H04L2012/40215G06F18/2411G06F18/254G06F18/24G06F18/214
Inventor 马从国郇小城李训豪严航丁晓红陈亚娟邬清海王建国
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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