A crop growth model selection method and device based on a neural network

Abstract

The invention discloses a crop growth model selection method and device based on a neural network, characterized in that a current crop growth model is simulated from the perspective of machine learning, and a comprehensive model library of crops is constructed, so that because of that there is no growth model for the current crop, the crop growth process data can be obtained after trial planting.According to the present invention, a most matched growth model is obtained through simulation verification of the model library; a machine learning library based on a crop growth model is realized by combining a neural network technology; and finally, a neural network is used to predict which model most accords with agricultural production requirements, so that the growth data of the crops withsmall planting areas and relatively weak planting benefits or other plants without appropriate growth models are generated, and the growth models generate the simulated planting data for soilless culture, greenhouse cultivation and intelligent agriculture as agricultural informationized and automatic data sources and can provide data reference for farmers to plant.

Description

technical field [0001] The present disclosure relates to the technical field of machine learning, in particular to a neural network-based crop growth model selection method and device. Background technique [0002] At present, the growth models of crops are all produced in laboratory experiments, and the experimental sites verify that these are obtained under the environment of large investment, long time consumption, and complex types of work. Therefore, species with high economic benefits, wide planting range, and easy promotion are selected. As a result, some plants with small planting areas and relatively weak planting benefits do not have a suitable growth model to provide reference. [0003] Due to the long period from the experiment to the production of the growth model, the large investment, etc., the crop coverage of the growth model is small. This disclosure realizes the simulation of the crop growth model from the perspective of machine learning, so as to provide ...

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Problems solved by technology

[0002] At present, the growth models of crops are all produced in laboratory experiments, and the experimental sites verify that these are obtained under the environment of large investment, long time consumption, and complex types of work. Therefore, species with high economic benefits, wide planting range, and easy promotion are selected. As a result, some plants with small planting areas and relatively weak planting benefits have no suitable growth model to provide reference

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