中文摘要：

针对农作物冠层图像颜色特征与缺素症状之间的模糊性和不确定性,利用模糊逻辑能够完整地表达领域推理规则和神经网络的自适应性,提出一种正则化的自适应模糊神经网络作为作物营养诊断分类决策模型。该模型能充分利用专家先验知识给出的＂if-then＂规则,完善网络的推理结构,并给出了网络规则层节点的自适应选取方法和相应的反向传播学习算法。通过对大豆缺素症状诊断试验表明,该模型速度快且稳定,精度接近100%,具有良好的适应性和实用性。

英文摘要：

Aiming at the ambiguity and uncertainty between nutrient deficiency and color characteristic of plant canopy image,a classification decision model based on regularized adaptive fuzzy neural network was set up to diagnose plant nutrition by using the complete rules of inference of fuzzy logic and adaptive of neural network.The ＂if-then＂ rules was fully used by the model,and the adaptive selection of law-level nodes and back propagation learning algorithm were given,meanwhile,network inference construction was perfected.The result of diagnosing soybean nutrient deficiency showed that the accuracy can be reach to 100%,meanwhile,the model has many advantages such as fast speed,stable,high precision,good robustness,as well as good adaptability and practical applicability.