中文摘要：

针对传统苹果专家病害诊断系统自学习能力差、准确率低的不足，研究并设计了人工神经网络的苹果病害诊断方法。采用动态编码对苹果病害模糊知识进行量化，构建BP网络诊断模型进行诊断；采用Java语言开发基于Web的病害动态诊断平台，用白水苹果病害样本进行了实验。该方法对20种苹果病害的诊断具有较好的效果，测试准确率达到85．4％。在获得必要领域知识的前提下，用神经网络进行苹果病害诊断准确率高，系统设计灵活，基于Web的诊断平台便于推广和使用。

英文摘要：

Aimed at the weak self - learning ability of traditional apple expert system, research and design an apple intelligent diagnosis system based on BP Artificial Neural Network （ANN）. Constructed BP network diagnosis model with dynamic coding method to quantize. Developed a dynamic diagnosis platform for apple diseases based on Baishui Apple. There is a perfect effect on 20 kinds of apples, diagnosis accuracy rating reached 85.4%. The analyses of the diagnosis results of typical examples indicate that this system has stable reliability, can simulate the expert diagnosis process adequately, and can improve the diagnosis efficiency greatly.