Keywords
BP neural network
Combination model
Drosophila algorithm
Grey prediction model
How to Cite
Abstract
This article first integrates data on fruit tree yield and related influencing factors in Fujian Province, covering two major categories of factors: social and natural. On this basis, this article calculated the correlation coefficients between fruit tree yield and various factors, verifying the rationality of indicator selection. Subsequently, this article used a combination of grey model GM (1,1), BP neural network model, and fruit fly algorithm to optimize the grey model and neural network for fruit tree yield prediction. In the end, based on the research results, it was found that the combination model of fruit fly algorithm optimized grey model and neural network has a better prediction effect on fruit tree yield, which is more suitable for us to deeply understand the changes in fruit tree yield. It can also be well trained for relatively random natural factors.
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