基于卷积神经网络的苹果栽培品种识别  

Apple Cultivar Identification Based on Convolutional Neural Network

作　　者：仇誉 韩俊英[1] 封成智 陈永卫 QIU Yu;HAN Jun-ying;FENG Cheng-zhi;CHEN Yong-wei(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 733070,China)

机构地区：[1]甘肃农业大学信息科学技术学院,甘肃兰州733070

出　　处：《计算机与现代化》2021年第12期65-71,共7页Computer and Modernization

基　　金：甘肃省自然科学基金资助项目(20JR5RA023);甘肃农业大学青年导师基金资助项目(GAU-QDFC-2019-04)。

摘　　要：针对苹果栽培品种识别分类问题,提供一个包含多个苹果果树品种的叶片图像原始数据集,并且研究构建一种新的深度卷积神经网络分类模型,对其分类准确性、泛化性能和稳定性进行对比验证,以期对苹果栽培品种简便、快速、准确的识别分类提供理论依据和技术支持。以甘肃省平凉市静宁县果树果品研究所苹果良种苗木繁育基地作为实验基地,在其中选取14个苹果果树品种。每个品种选取10棵左右树龄、树势、长势都存在差异的果树,采摘100片左右成熟的、无机械损伤的叶片,然后拍摄叶片图像建立数据集,进而利用卷积神经网络训练识别分类模型。本文针对苹果栽培品种识别分类,提供一个包含14个苹果果树品种共计14394张叶片图像的原始数据集,并且设计实现基于卷积神经网络的识别分类模型。实验结果表明,该识别分类模型有较高的准确率,训练集训练精度可以达到99.88%,验证集验证精度为94.36%,独立测试集的测试精度为90.49%。本文的研究结果可以为现代苹果田间种植及科研试验等实际场景提供力所能及的帮助,为深度卷积神经网络技术在植物品种识别分类实际应用场景提供参考,丰富深度学习在农业上的应用。Against apple fruit varieties identification and classification problem,a original data set containing more than one apple fruit varieties of leaf image is provided,a new convolution model of neural network classification is built to verify the classification accuracy,generalization performance and stability,which provides theoretical basis and technical support for apple cultivars’simple,rapid,accurate and reliable identification and classification.Taking the apple fine seedling breeding base of Jingning Fruit Research Institute of Pingliang City of Gansu Province as the experimental base,14 apple tree varieties are selected.For each variety,about 10 fruit trees with different tree ages,tree size and growth status were selected,and about 100 mature leaves without mechanical damage are picked,and then leaf images are taken to form a data set.Then the convolutional neural network is used to train the recognition and classification model.Aiming at the recognition and classification of apple cultivars,this paper provides an original data set containing 14394 leaf images of 14 apple and fruit cultivars,and designs and implements a recognition and classification model based on convolutional neural network.The experimental results show that the model has high accuracy.The training accuracy of the training set can reach 99.88%,the verification accuracy of the verification set is 94.36%,and the test accuracy of the independent test set is 90.49%.The results of this study can help the modern apple field planting,scientific research experiments and other practical scenarios,and provide a reference for the practical application of deep convolutional neural network technology in plant variety identification and classification,and enrich the application of deep learning in agriculture.

关 键 词：卷积神经网络 苹果果树品种 叶片图像 识别分类 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]