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- Title
基于果穗图像的玉米品种分类识别.
- Authors
赵威; 马睿; 王佳; 郭宏杰; 许金普
- Abstract
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.
- Subjects
OPTIMIZATION algorithms; CULTIVARS; GERMPLASM; AGRICULTURAL productivity; TECHNOLOGY transfer; CORN seeds; HEBBIAN memory
- Publication
Journal of Agricultural Science & Technology (1008-0864), 2023, Vol 25, Issue 6, p97
- ISSN
1008-0864
- Publication type
Article
- DOI
10.13304/j.nykjdb.2022.0633