基于深度学习的葡萄砧木叶片识别研究

Research on Grape Rootstock Leaf Recognition Based on Deep Learning

嫁接有利于增强树体对生物及非生物胁迫的适应能力,提高葡萄产量和品质。葡萄砧木品种多样复杂,识别难度较大,深度学习能够快速提取图像的深层特征,被广泛应用于植物图像分类识别领域。本研究以30份葡萄砧木成龄叶图像作为研究对象,通过采集叶片图像,构建了一个包含13547张的葡萄砧木叶片图像的数据集。采用GoogleNet、ResNet-50、ResNet-101以及VGG-16等4个卷积神经网络对其进行自动识别。结果表明:精度最高的分类网络为ResNet-101,在最优模型参数(学习率:0.005,最小批次:32,迭代次数:50)下精度达到97.5%。ResNet-101模型检测的30个品种中,平均预测精确率为92.59%,有7个品种的预测精确率达到100%;平均召回率为91.08%,有8个品种的召回率达到100%,叶片的叶面纹理、叶脉以及叶缘部分对品种识别的影响最大。以上结果证实,深度学习网络模型可以实现对葡萄砧木的自动实时识别,为葡萄砧木品种的保护、利用、分类研究以及其他农作物的品种识别提供参考。

Grafting is beneficial in enhancing the adaptability to biotic and abiotic stresses,and improving the yield and quality of grapes.There are varieties of grape rootstocks,while their precise clarification become complex and difficult.Deep learning,capable of rapidly capturing deep features from images,has been extensively applied in the field of plant image classification and recognition.In this study,the mature leaf images of 30 grape rootstocks were deployed to construct a dataset,comprising 13547 grape rootstock leaf images.Four convolutional neural networks,GoogleNet,ResNet-50,ResNet-101 and VGG-16,were used for image recognition.The results show that the classification network with the highest accuracy is ResNet-101,and the accuracy reaches 97.5%under the optimal model parameters(learning rate:0.005,mini-batch size:32,Max epochs:50).Among the 30 varieties,the average prediction precision rate was 92.59%,and the prediction precision reaching 100%was observed in seven varieties.The recall rate of eight varieties reached 100%,and the average recall rate was 91.08%.The leaf surface texture,leaf vein and leaf margin were major factors that influence variety classification.This study confirmed the application capacity of deep learning network models in real-time automatic identification of grape rootstocks,thus providing reference for the protection,utilization,clasification research of grape rootstock varieties and the variety recognition of other crops.