基于迁移学习的小麦籽粒品种识别研究

Study on wheat seed variety identification based on transfer learning

不同角度下小麦籽粒具有不同特征信息,造成分类结果存在差异。采用同一小麦籽粒多角度图片,使用小麦籽粒腹沟向上、腹沟向下和腹沟朝前三个角度图片构建小麦籽粒品种数据集。选取黄淮麦区种植面积较大的6个小麦品种作为试验材料,对比不同模型在小麦籽粒识别上的准确度。采用VGG-16、ResNet-50、Inception-V3卷积神经网络,通过迁移学习的方式建立小麦籽粒品种识别分类模型,验证集识别准确率最高为99.35%,高于不迁移学习的识别方法和传统机器学习的识别方法。在相同的试验条件下,三种模型在使用迁移学习的情况下对小麦籽粒识别的测试集准确率分别达到99.55%、99.77%、99.22%,优于单面特征建模识别。基于3种模型中分别选择最优试验,对其3种角度分别识别。结果表明:腹沟向下的识别率在3种模型中最好,腹沟朝前次之,腹沟向上较差。通过试验发现,采用同一小麦籽粒多角度图片可以更准确地提取小麦籽粒特征,并且有助于分类模型提升品种识别准确率。

Wheat grains under different angles have different feature information,resulting in differences in classification results.In this paper,we adopt the same wheat grain multi-angle pictures,and use the three angle pictures of wheat grain groove upward,groove downward and groove forward to construct the wheat grain variety dataset.Six wheat varieties with large planting area in Huanghuai wheat area were selected as test materials to compare the accuracy of different models in wheat grain recognition.VGG-16,ResNet-50,and Inception-V3 convolutional neural networks are used to build a classification model for wheat seed variety recognition by transfer learning,and the highest recognition accuracy of the validation set is 99.35%,which is higher than that of the recognition method without transfer learning and the traditional machine learning recognition method.Under the same test conditions,the test set accuracies of the three models for wheat seed grain recognition using migration learning reached 99.55%,99.77%,and 99.22%,respectively,which were better than single-sided feature modeling recognition.Based on the selection of the optimal test among each of the three models,their three angles were recognized separately.The results showed that the recognition rate of ventral groove downward was the best among the three models,ventral groove toward the front was the second best,and ventral groove upward was poor.It was found that the use of multi-angle pictures of the same wheat grain can extract wheat grain features more accurately and help the classification model to improve the accuracy of variety identification.