摘要
【目的】建立基于深度学习的鲜食葡萄叶片品种识别模型,为中国鲜食葡萄品种的保护、开发和系统分类工作奠定科学基础,并为其他农作物品种的鉴别工作提供借鉴。【方法】聚焦于68个常见鲜食葡萄成熟叶片的图像分析,通过图像采集技术,构建一个包含29713张图像的鲜食葡萄叶片数据集。采用4种先进的卷积神经网络模型——GoogleNet、ResNet-50、ResNet-101和VGG-16对这些图像进行深入的自动识别研究。【结果】在所有测试的网络模型中ResNet-101表现最为出色,其在最佳参数配置下达到了97.99%的分类精度。在对68个葡萄品种的叶片图像进行检测时,对23个品种的预测准确率达到了100%,对整体的平均预测准确率则为94.90%。此外,对18个品种的召回率也达到了100%,对整体的平均召回率为94.19%。采用Grad-CAM技术分析的结果显示,所有模型都能精确地识别叶片的关键特征,叶片的表面纹理、叶脉和叶缘部分在品种识别过程中起到了至关重要的作用。【结论】深度学习网络模型可以很好地对鲜食葡萄进行自动实时识别。
【Objective】With the continuous development of grape varieties,problems such as variety confusion and inaccurate identification would occur in the actual production and scientific research work,and accurate identification of grape varieties has become more and more difficult It is urgent to explore a nondestructive,efficient and environment-friendly identification method.This work amied to provide reference for the protection,utilization,and classification research of table grape varieties.【Methods】In this study,the leaf images were taken in the National Grape Germplasm Nursery(Zhengzhou)of Zhengzhou Fruit Tree Institute,Chinese Academy of Agricultural Sciences.The images of mature leaves of the 68 common table grapes were taken under natural condition in the field.The leaves were fully expanded without obvious symptoms of nutrition deficiency,pathogen infection and insect damage.The sampling position was at the 7th-9th nedes of the new shoots.The front images of different leaves were taken,and a dataset of 29713 fresh grape leaves was constructed.In the realm of automatic recognition,four distinct convolutional neural network models were deployed:GoogleNet,ResNet-50,ResNet-101,and VGG-16.【Results】Through fair comparison of all convolutional neural networks,under optimal parameters,ResNet-101 performed best in the identification of table grapes,with an accuracy of 97.13%,ResNet-50 was slightly lower,with an accuracy of 97.06%,and VGG-16 and GoogleNet models had an accuracy of less than 95%.When ResNet-101 was used as the classification model,the optimized parameters were the learning rate of 0.005,the minimum batch of 32,and the number of iterations was 50.Under this parameter,the classification performance was the best,and the classification accuracy was as high as 97.99%.The model accuracy and LOSS value of ResNet-101 model was significantly higher than those of other models.The initial accuracy was the highest,the convergence was faster and more stable,the final accuracy was the highest,the initial LOSS was the lowest,the LOSS decreased faster,and the final LOSS was relatively stable.Among the 68 varieties identified by the ResNet-101 model,the prediction accuracy of the 23 varieties was 100%,and the average recognition accuracy of the 68 varieties reached 94.90%;The prediction accuracy of the ResNet-50 for the 13 varieties was 100%,and the average recognition accuracy of the 68 varieties reached 90.38%;The prediction accuracy of the VGG-16 for the 11 varieties was 100%,and the average recognition accuracy of the 68 varieties was 85.45%;The prediction accuracy of the GoogleNet model was 100% for only 5 varieties,and the average recognition accuracy of the 68 varieties was 78.79%.In contrast,the prediction accuracy of the ResNet-101 model was significantly higher than that of the ResNet-50,GoogleNet and VGG-16 models,and the difference in recognition accuracy between varieties was smaller and more stable.In ResNet-101 model,the Recall rate of the 18 varieties reached 100%,and the average Recall rate of the 68 varieties reached 94.19%;In the ResNet-50 model,the Recall rate of the 6 varieties reached 100%,and the average Recall rate of the 68 varieties reached 88.71%;The average Recall rate of the 68 varieties of VGG-16 was 82.83%;The GoogleNet had only two varieties,Crimson Seedless and sunshine rose,with a Recall rate of 100%,and the average Recall rate of the 68 varieties was 74.44%.In contrast,the ResNet-101 model was significantly better than the ResNet-50 GoogleNet,VGG-16.The Recall rate among the varieties was more stable.The F_(1)value of creson seedless,Xianfeng and longan varieties in the ResNet-101 model reached 1,and the average F_(1)value reached 0.94.The difference of the F_(1)value among the varieties was small,the reliability of the model was high,and the model effect was more stable.The F_(1)value of VGG-16 model for Crimson Seedless reached 1,and the average F_(1)value reached 0.82;No F_(1)value of the ResNet-50 and GoogleNet models reached 1,and their average F_(1)values were 0.88 and 0.74,respectively.The Grad-CAM algorithm was used to output the weighted gradient heat map in the final accretion layer,and the network model was visualized.The results showed that the four convolutional neural networks could accurately identify the main characteristics of the leaves,and the leaf texture,vein and edge of the leaves had the greatest impact on variety recognition.【Conclusion】The ResNet-101 model had the highest overall recognition accuracy,the lowest LOSS value,the higher average recognition accuracy and Recall rate of varieties,and could get a better model with fewer iterations,which would take less time.The Grad-CAM algorithm was used to evaluate the classification effect of four convolutional neural networks,and all of them could accurately identify the main features of the leaves.The rapid and accurate recognition of the table grapes was realized.Therefore,the deep learning network model could complete the automatic real-time recognition of the table grapes.
作者
潘博文
林美苓
鞠延仑
苏宝峰
孙磊
樊秀彩
张颖
张永辉
刘崇怀
姜建福
房玉林
PAN Bowen;LIN Meiling;JU Yanlun;SU Baofeng;SUN Lei;FAN Xiucai;ZHANG Ying;ZHANG Yonghui;LIU Chonghuai;JIANG Jianfu;FANG Yulin(Zhengzhou Fruit Research Institute,Chinese Academy of Agricultural Sciences,Zhengzhou 450009,Henan,China;College of Enology,Northwest A&F University,Yangling 712100,Shaanxi,China;College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100 China,Shaanxi,China;Tropical Eco-Agriculture Research Institute,Yunnan Academy of Agricultural Sciences/Yuanmou Dry-Hot Valley Botancal Garden,Yuanmou 651300,Yunnan,China)
出处
《果树学报》
北大核心
2025年第8期1883-1896,共14页
Journal of Fruit Science
基金
广西重点研发计划(桂农科AB241484010)
国家现代农业产业技术体系(CARS-29-yc-1)
楚雄州“兴楚科技领军人才”培养项目(CXKJLJRC2023—06号)
国家园艺种质资源库运行服务(NHGRC2021-NH00-2)
中国农业科学院科技创新工程专项经费(CAAS-ASTIP-2017-ZFRI)。
关键词
鲜食葡萄
深度学习
品种识别
叶片
Table grape
Deep learning
Variety identification
Leaves
