基于实例推理及其在农业病虫害诊断与防治中的应用研究

规则推理等传统推理方法的推理性能严重依赖于知识的完备程度。在农业领域中。知识获取和知识表示已成为推理的主要瓶颈之一，因此利用传统推理方法建立的专家系统效率低、鲁棒性不好。基于案例推理的专家系统能充分利用已有的实例经验，具有自学习功能，从而推理效率高，鲁棒性能较好。本研究将案例推理专家系统应用于作物的病虫害防治和诊断．得出了基于案例推理农业病虫害诊断与防治专家系统的知识表示和推理机制，并在实际应用中获得了较好的效果。

Case-Based Reasoning Expert System to Support the Diagnosis and Prevention of Diseases and Pests

Diagnosis and prevention of diseases and pests is so important to high yield of tomatoes that it is key of the tomato planting. Now digging the special knowledge, the computer engineer is in difficulties so that there is no much knowledge to be summarized into rules. But, the performance of RBR ES (Rule-Based Reasoning Expert System) is close relative to the rules' number and knowledge's integrity. So, the RBR ES in agriculture will hardly avoid some disadvantages such as low reasoning efficiency and bad robust. In this article, we discuss and research a knowledge expression and reasoning principle of the CBR ES (Case-Based Reasoning Expert System) to apply to diagnosis and prevention of diseases and pests.