School of Information Engineering, Huzhou University, Huzhou, China.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China.
J Sci Food Agric. 2024 Oct;104(13):8332-8342. doi: 10.1002/jsfa.13668. Epub 2024 Jun 21.
Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting.
This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset.
The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.
不同品种的水稻在种植时间、抗逆性等方面存在差异。随着水稻育种技术的进步，水稻品种的数量显著增加，品种识别对于交易和种植都至关重要。
本研究采集了 20 个杂交水稻种子品种的 RGB 图像。采用增强型深度超分辨率网络（EDSR）提高图像分辨率，利用高分辨率数据集的品种分类模型的性能优于利用低分辨率数据集的模型。提出了一种新的训练样本选择方法，将深度学习与 Kennard-Stone（KS）算法相结合。卷积神经网络（CNN）和自动编码器分别作为有监督和无监督的特征提取器。提取的特征向量随后由 KS 算法处理以选择训练样本。与随机选择方法相比，所提出的方法在水稻品种分类中表现出更好的性能，总体分类准确性提高了约 10.08%。此外，通过向图像中引入噪声来研究所提出的方法对噪声的影响，与随机选择方法相比，所提出的方法在噪声图像数据集上仍保持更好的性能。
实验结果表明，监督学习和无监督学习模型都可以有效地作为特征提取器，深度学习框架对训练集样本的选择有显著影响。本研究提出了一种新的分类任务训练样本选择方法，并提出将该方法扩展到图像数据集和其他类型数据集的潜力。值得进一步探索这一潜力。© 2024 化学工业协会。
