Traditional pest control approaches rely mostly on the experience of farmers, which may not be effective due to lack of scientific information regarding the environment where crops grow. Farmers can initiate a more effective integrated pest management program when precise and quantified results of forecasting pest population outbreaks are provided. Previous studies generally utilize long-term data to predict pest populations, but such a prediction approach might not be useful for farmers who grow fruit and vegetables with shorter life cycles. This paper therefore proposes an interval type-2 fuzzy logic system (IT2FLS) with short-term data to forecast the population dynamics of the oriental fruit fly (OFF, Bactrocera dorsalis (Hendel)) and the tobacco cutworm (TC, Spodoptera litura (Fabricius)). Two automatic monitoring systems are used to collect the data of the population dynamics of OFFs and TCs and the environmental parameters in farming areas. A univariate fuzzy time series forecasting model with difference-based intervals (UFTSFM_DI) and a bivariate fuzzy time series forecasting model with difference-based intervals (BFTSFM_DI) are developed, and integrated into the proposed IT2FLS. It is found that the BFTSFM_DI model yields better performances of forecasting OFF and TC populations when the atmospheric temperature data are employed. With the forecasting results, farmers will have a better understanding of the population dynamics of the OFF and TC in farming areas, so they can take proper measures, such as bagging their fruits and spraying pesticides, before pest outbreaks occur.

传统的害虫防治方法主要依赖于农民的经验，由于缺乏有关作物生长环境的科学信息，这种方法可能无效。当提供预测害虫种群爆发的准确和量化结果时，农民可以启动更有效的害虫综合管理计划。以前的研究通常利用长期数据来预测害虫种群，但这种预测方法可能对种植生命周期较短的水果和蔬菜的农民没有用处。因此，本文提出了一个区间类型 2 模糊逻辑系统 (IT2FLS) 与短期数据来预测东方果蝇 (OFF, Bactrocera dorsalis (Hendel)) 和烟草地老虎 (TC, Spodoptera litura ) 的种群动态。（法布里修斯））。两个自动监测系统用于收集OFFs和TCs的种群动态和农业区域的环境参数数据。开发了具有基于差异的区间的单变量模糊时间序列预测模型 (UFTSFM_DI) 和具有基于差异的区间的双变量模糊时间序列预测模型 (BFTSFM_DI)，并将其集成到建议的 IT2FLS 中。研究发现，BFTSFM_DI模型在使用大气温度数据时预测OFF和TC人口的性能更好。借助预测结果，农民将更好地了解农田中 OFF 和 TC 的种群动态，从而在病虫害发生之前采取适当的措施，例如装袋水果和喷洒杀虫剂。