To improve the accuracy of forecasting the peak occurrence of Dendrolimus punctatus Walker, we here used the Bayes discriminant analysis to predict this peak occurrence for the first and second generation of Dendrolimus punctatus larvae based on these data from 1983 to 2016 in Qianshan County, Anhui Province. Our present results showed that this discriminant equation for the first generation was as follows: f⁽¹⁾ = −3.2588‐6.2700x₁ + 1.2870x₂ + 0.7920x₃ + 0.4152x₄; f⁽²⁾ = −14.5215‐8.5710x₁ + 2.9790x₂ + 2.0280x₃ + 0.5031x₄; f⁽³⁾ = −3.5264; f⁽⁴⁾ = −66.8312‐12.5216x₁ + 5.1740x₂ + 4.7162x₃ + 0.6033x₄. And that the prediction accuracy for the first generation was 97.22%. Whilst this discriminant equation for the second generation was as follows: f⁽¹⁾ = −3.536‐1.192x₅ + 1.338x₆ + 0.638x₇₋0.025x₈; f⁽²⁾ = −7.317‐1.337x₅ + 4.240x₆ + 1.010x₇₋0.295x₈; f⁽³⁾ = −16.488‐3.192x₅ + 4.955x₆ + 1.900x₇–0.411x₈; f⁽⁴⁾ = −34.502‐4.184x₅ + 7.484x₆ + 2.583x₇–0.443x₈. The prediction accuracy for the second generation was 85.71%. Overall, our findings revealed that the Bayes discriminant analysis could screen out key factors to significantly improve the prediction accuracy of peak occurrence of Dendrolimus punctatus larvae.

中文翻译：

---

基于贝叶斯判别法的马尾松毛虫幼虫高峰发生预测

为了提高预测马尾松毛虫峰值发生的准确性，我们根据这些数据从1983年至2016年在安徽省潜山县的数据，使用Bayes判别分析来预测第一代和第二代马尾松幼虫的峰值发生。。我们目前的结果表明，第一代判别方程如下：f ^（1）  = −3.2588-6.2700 x ₁  + 1.2870 x ₂  + 0.7920 x ₃  + 0.4152 x ₄ ; f ^（2）  = −14.5215‐8.5710 x ₁  + 2.9790 x₂  + 2.0280 x ₃  + 0.5031 x ₄ ; f ^（3）  = −3.5264; f ^（4）  = -66.8312-12.5216 x ₁  + 5.1740 x ₂  + 4.7162 x ₃  + 0.6033 x ₄。并且第一代的预测准确性为97.22％。第二代判别式如下：f ^（1）  = −3.536‐1.192 x ₅  + 1.338 x ₆  + 0.638 x ₇₋ 0.025 x ₈ ; F^（2）  = −7.317‐1.337 x ₅  + 4.240 x ₆  + 1.010 x ₇₋ 0.295 x ₈ ; ˚F ^（3）  = -16.488-3.192 X ₅  + 4.955 X ₆  + 1.900 X ₇ -0.411 X ₈ ; f ^（4）  = −34.502‐4.184 x ₅  + 7.484 x ₆  + 2.583 x ₇ –0.443 x ₈。第二代的预测准确性为85.71％。总体而言，我们的发现表明，贝叶斯判别分析可以筛选出关键因素，从而显着提高马尾松毛虫幼虫高峰出现的预测准确性。