Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach

Low temperature chilling damage is one of the most serious disasters in maize production, which is a typical non-linear complex issue with numerous influencing factors and strong uncertainty. How to predict it is not only a hot theoretical research topic, but also an urgent practical problem to be solved. However, most of the current researches are focusing on post-disaster static descriptive assessment rather than pre-disaster dynamic predictive analysis, resulting in the problems such as no indicative result and low accuracy. In this study, the satisfaction rate of environmental accumulated temperature for maize production was used to measure the chilling damage risk, and a model for maize chilling damage risk prediction based on probabilistic neural network was constructed. The model was composed of input layer, pattern layer, summation layer and output layer. The obtained results showed that the prediction accuracy for the most serious risk level was as high as 0.91, and the rates of the Type I Error and Type II Error made by the model were 0.1 and 0.09, respectively. This indicated that the model employed was promising with good performance. The results of this research is are of both theoretical significance for providing a new reference method of pre-disaster prediction to study maize chilling disaster risk and practical significance for reducing maize production risk and ensuring yield safety.