计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 113-117.doi: 10.11896/jsjkx.201100119
韩斌1, 曾松伟1,2
HAN Bin1, ZENG Song-wei1,2
摘要： 植物叶片识别是植物自动分类识别研究的重要分支和热点,利用卷积神经网络进行图像分类研究已成为主流。为了提高植物叶片识别准确率,提出了基于多特征融合和卷积神经网络的植物叶片图像识别方法。首先对植物叶片图像进行预处理,提取LBP特征和Gabor特征,将多特征相加融合输入网络进行训练,使用卷积神经网络(AlexNet)构架作为分类器,利用全连接层对植物叶片进行识别。为了避免过拟合现象,使用“dropout”方法训练卷积神经网络,通过调节学习率、dropout值、迭代次数优化模型。实验结果表明,基于多特征融合的卷积神经网络植物叶片识别方法对Flavia数据库32种叶片和MEW2014数据库189种叶片识别分类效果较好,平均正确识别率分别为93.25%和96.37%,相比一般的卷积神经网络识别方法,该方法可以提高植物叶片的识别准确率,鲁棒性更强。
中图分类号:
| [1] DONG H X,GUO S Y.Plant leaf classification method combining shape and texture features[J].Computer Engineering and Applications,2014,50(23):185-188. [2] XIAO X Y.Study on features of plant leaf image recognition and realization of online recognition system[D].Hefei:University of Science and Technology of China,2011. [3] WANG Z,CHI Z,FENG D,et al.Leaf image retrieval withshape features[J].Lecture Notes in Computer Science,2000,1929:477-487. [4] NETO J C,MEYER G E,JONES D D,et al.Plant species identification using elliptic Fourier leaf shape analysis[J].Compu-ters & Electronics in Agriculture,2006,50(2):121-134. [5] COPE J S,REMAGNINOP,BARMAN S,et al.Plant texture classification using gabor co-occurrences[M].Berlin Heidelberg:Springer,2010:669-677. [6] CHAKI J,PAREKH R,BHATTACHARYA S.Plant leaf recognition using texture and shape features with neural classifiers[J].Pattern Recognition Letters,2015,58:61-68. [7] WANG L J,HUAI Y J,PENG Y C.Method of identification of foliage from plants based on extraction of multiple features of leaf images[J].Journal of Beijing Forestry University,2015,37(1):96-104. [8] DU J X,ZHAI C M,WANG Q P.Recognition of plant leafimage based on fractal dimension features[J].Neurocomputing,2013,116(10):150-156. [9] WU S G,BAO F S,XU E Y,et al.A leaf recognition algorithm for plant classification using probabilistic neural network[C]//2007 IEEE international symposium on signal processing and information technology.Cairo:IEEE,2007:11-16. [10] LIU N,KAN J M.Plant leaf identification based on the multi-feature fusion and deep belief networks method[J].Journal of Beijing Forestry University,2016,38(3):110-119. [11] WANG X F,HUANG D S,DU J X,et al.Classification of plant leaf images with complicated background[J].Applied Mathematics and Computation,2008,205(2):916-926. [12] NOVOTNY P,SUK T.Leaf recognition of woody species incentral Europe[J].Biosystems Engineering,2013,115(4):444-452. [13] WIATOWSKI T,BOLCSKEI H.A mathematical theory of deep convolutional neural networks for feature extraction[J].IEEE Transactions on Information Theory,2018,64(3):1845-1866. [14] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe:ACM,2012:1097-1105. [15] LU H T,ZHANG Q C.Applications of deep convolutional neural network in computer vision[J].Journal of Data Acquisition and Processing,2016,31(1):1-17. [16] LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [17] YE F M,DONG M,LUO W,et al.Agricultural remote sensing image retrieval based on convolutional neural network and reranking[J].Transactions of the Chinese Society of AgriculturalEngineering,2019,35(15):138-145. [18] YUAN P,LI W,REN S G,et al.Recognition for flower type and variety of chrysanthemum with convolutional neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(5):152-158. [19] FU L S,FENG Y L,ELKAMIL T,et al.Image recognitionmethod of multi-cluster kiwifruit in field based on convolutional neural networks[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(2):205-211. [20] LONG M S,OUYANG C J,LIU H,et al.Image recognition of Camellia oleiferadiseases based on convolutional neural network &transfer learning[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(18):194-201. [21] SUN J,TAN W J,MAO H P,et al.Recognition of multiple plant leaf diseases based on improved convolutional neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(19):209-215. [22] JEON W,RHEE S.Plant leaf recognition using a convolution neural network[J].The International Journal of Fuzzy Logic and Intelligent Systems,2017,17(1):26-34. [23] ZHANG S,HUAI Y J.Leaf image recognition based on layered convolutions neural network deep learning[J].Journal of Beijing Forestry University,2016,38(9):108-115. [24] LEE S H,CHAN C S,WILKIN P,et al.Deep-plant:plant identification with convolutional neural networks[C]//Proceedings of 2015 IEEE International Conference on Image Processing (ICIP).Quebec:IEEE,2015:452-456. [25] CHEN M S,CAI Z S.Study on fusion of visual and infrared images based on NSCT[J].Laser&Optoelectronics Progress,2015,52(6):061002. [26] ZHANG L B,ZHANG J.A new adaptive fusion method based on saliency analysis for remote sensing images[J].Chinese Journal of Lasers,2015,42(1):0114011. [27] YU H L,MA J W,ZHANG Y Z.Plant leaf recognition model based on two-way convolutional neural network[J].Journal of Beijing Forestry University,2018,40(12):132-137. [28] SHI L,WANG Y M,CAO Y J,et al.Vehicle Type Recognition Based on Deep Convolution Neural Network[J].Computer Science,2018,45(5):280-284. [29] ZHENG Y L,ZHONG G L,WANG Q,et al.Method of LeafIdentification Based on Multi-feature Dimension Reduction[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):30-37. |
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