BP-GA-based greenhouse crop plantation efficiency condition optimization method and system

Abstract

The invention provides a BP-GA-based greenhouse crop plantation efficiency condition optimization method and system. The method comprises the following steps: acquiring sample data of greenhouse crops; according to the acquired sample data, constructing a BP neural network model of a three-layer topology structure; training the three-layer BP neural network model by use of the sample data, and mining a function relation between an input variable and an output variable; and according to the function relation and a preset scoring weight, obtaining an integrated index E, and carrying out extremum searching optimization on the function relation by use of GA so as to obtain a decision parameter, wherein the decision parameter is a corresponding input variable combination when the integrated index E is the optimal. According to the invention, an optimal decision parameter combination for improving the greenhouse crop plantation efficiency can be timely grasped, and accurate prediction can be performed; and adjustment measures can be taken timely, key factors influencing the plantation efficiency can be reasonably controlled, considerable plantation output is guaranteed, the quality is improved, resources are effectively saved, and the production cost is decreased.

Description

technical field [0001] The invention relates to the technical field of agricultural planting, in particular to a BP-GA-based method and system for optimizing conditions for planting efficiency of greenhouse crops. Background technique [0002] At present, the improvement of agricultural planting efficiency and the prediction of crop yield are an important research work for my country's agricultural development. It is a large system related to multiple factors such as market research, planting scale, and climate regulation. Management is instructive. [0003] Since the yield of crops is not only affected by natural factors such as soil, fertility, and climate, but also by social and economic factors such as policies, investment, and technological level, the traditional farming mode that mainly relies on experience to fertilize and irrigate not only wastes a lot of manpower and material resources , and it does not play any role in the prediction and analysis of agricultural pl...

AI Technical Summary

Problems solved by technology

[0003] Since the yield of crops is not only affected by natural factors such as soil, fertility, and climate, but also by social and economic factors such as policies, investment, and technological level, the traditional farming mode that mainly relies on experience to fertilize and irrigate not only wastes a lot of manpower and material resources , and does not play any role in the prediction and analysis of agricultural planting efficiency and yield

Although some scholars have used some relatively complex mathematical calculations and equations to propose various evaluation systems and methods for agricultural planting efficiency, they are limited to the research on corresponding steps such as irrigation and fertilization. There is still no relatively complete method for evaluating the comprehensive index of agricultural planting efficiency.

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