摘要
提出了一种基于模糊BP综合神经网络的田间杂草分类识别方法。对分类特征进行模糊化处理，充分考虑了杂草的分类特征本身存在的不确定性。使用遗传算法对网络结构进行优化处理，提高了该综合神经网络的收敛性和稳定性。并基于特征级数据融合方法进行杂草识别。对田间7种杂草进行识别的实验结果表明，7种杂草的混合识别率达到94．2％；另外，对玉米及其伴生杂草进行分类测试，混合识别率达到96．7％，具有较好的识别精度。
A novel weed recognition scheme based on fuzzy BP overall neural network is proposed. First, the classification features are blurred to deal with the uncertainty of weed features. Second, the genetic algorithm is used to optimize the network structure so as to improve the network＇ s convergence and stability. Finally, a feature-level data fusion scheme is used. In weed species identification experiments, neural network consists of the 4 BP sub-networks on color feature, main texture feature, secondary texture feature and spectral feature. The results indicate that the overall recognition rate reaches to a good recognition accuracy of 94.2% for 7 weed species. Besides, experiments were put into effect on the corn and its accompanying weeds. The neural network consists of the 4 BP sub-networks on color feature, main texture feature, height feature and spectral feature. The recognition rate reaches to 96.7% with a better recognition accuracy.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2014年第3期275-281,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
中央高校基本科研业务费专项资金资助项目（DL12DB06）
