为了实现针叶苗木分级特征的提取，提出了基于模糊神经网络 (FNN)的地径自动搜索定位 (RC- ASL)方法，同时提出了针叶苗木图象行像素特征向量的构造方法，并应用相应的隶属函数实现了各特征量的模糊化过程.经过网络的学习和训练，得到了用于实现 RC- ASL 方法的 FNN结构.实验结果表明，该方法的定位精度能够满足实际应用的要求

Accurately locating the root collar of seedling is important for the measurement of seedling morphological characteristics in the computer vision system of seedling grading. A RC ASL ( Root Collar Automatic Searching and Locating ) method based on fuzzy neural network (FNN) has been studied to resolve the key problem in the field of conifer seedling feature extraction. In the computer vision system, the features of the object may be extracted based on the hue variation of the image. Three line features of the seedling image are selected based on the hue variation in every line of the image as the input vector of the FNN, And the corresponding membership function has been chosen to accomplish the fuzzy processing of these features. BP algorithm is selected to optimize the weights of the membership function and the stimulation function of the neural networks. With the training of the FNN, a FNN structure used to search and locate the root collar of seedling is achieved. The foliage, stem, root and the root collar of the conifer seedlings is recognized from the seedling image and then the root collar is located by the FNN. The experimental results show that this method has the capability to meet the requirement of seedling grading operation.