التفاصيل البيبلوغرافية
| العنوان: |
基于果穗图像的玉米品种分类识别. (Chinese) |
| Alternate Title: |
Classification and Identification of Corn Varieties Based on Ear Image. (English) |
| المؤلفون: |
赵威, 马睿, 王佳, 郭宏杰, 许金普 |
| المصدر: |
Journal of Agricultural Science & Technology (1008-0864); 2023, Vol. 25 Issue 6, p97-106, 10p |
| مصطلحات موضوعية: |
OPTIMIZATION algorithms, CULTIVARS, GERMPLASM, AGRICULTURAL productivity, TECHNOLOGY transfer, CORN seeds, HEBBIAN memory |
| Abstract (English): |
Crop variety plays a key role in improving agricultural production and income. Aiming at the safety problems of seed industry, in order to realize the rapid recognition and protection of corn varieties, a variety recognition model based on ear image was proposed. After image preprocessing, 1 000 images of corn ears were divided into training set, validation set and test set according to the ratio of 7∶2∶1. And the data sets were enhanced by translation, flipping and other data processing. Using transfer learning technology, the pre-trained weights and parameters were transferred to NASNet-mobile, Xception, ResNet50V2, MobileNetV2, DenseNet121 and VGG16 for comparative experiments. The results showed that the performance of NASNet-mobile was best, and the recognition rate reached 90%. At the same time, different optimization algorithms were used for comparative experiments, and the result showed that Adam model performed better. Based above results, experiments were carried out under a variety of different full connection layer modules. The results showed that, when the number of full connected layers was 2 and the dimension was 256, better corn ear image features could be obtained, and the recognition accuracy of the final model under the full connection layer module reached 95%, which increased by 5% compared with NASNet-mobile. It realized the variety classification and recognition of corn ear image, which provided intelligent technical support for the rapid and accurate identification of corn varieties and the protection of germplasm resources. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
优良品种对提高农业产量和收入起着关键作用, 针对现有的种业安全问题, 为实现玉米品种的快速识 别和保护, 构建一种基于玉米果穗图像的品种识别模型。将采集到的1 000张玉米果穗图像经预处理后按 7∶2∶1的比例划分为训练集、验证集和测试集, 并对数据集进行平移、翻转等多种数据增强处理。通过迁移学 习, 将预训练好的权重和参数迁移到NASNet-mobile、Xception、ResNet50V2、MobileNetV2、DenseNet121、VGG16 模型进行对比, 结果表明, NASNet-mobile识别性能较好, 识别率达90%。不同优化算法的对比表明, 优化器选 择Adam模型具有更好的表现。在此基础上, 对多种全连接层模块进行试验, 结果表明, 全连接层数量为2层、 维度为256 时可以得到更好的玉米果穗图像特征, 最终模型在全连接层模块下的识别准确率达95%, 较 NASNet-mobile提升5%, 实现了对玉米品种的分类识别。以上结果为玉米品种的快速精准鉴定以及种质资源 保护提供了智能化技术支持. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |