Li Xueyong, Zhai Mingjia, Zheng Liyuan, Zhou Ling, Xie Xiwang, Zhao Wenyi, Zhang Weidong
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China.
School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China.
Front Plant Sci. 2024 Apr 16;15:1376915. doi: 10.3389/fpls.2024.1376915. eCollection 2024.
Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.
玉米种子是农业生产中的重要元素，准确识别其品种和质量对于种植管理、品种改良以及农产品质量控制至关重要。然而，仅靠传统的人工分类方法已无法满足智慧农业的需求。随着深度学习方法在计算机领域的快速发展，我们提出了一种名为ERNet的高效残差网络来识别高光谱玉米种子。首先，我们使用线性判别分析对高光谱玉米种子图像进行降维处理，以便图像能够顺利输入网络。其次，我们使用有效的残差块从图像中提取细粒度特征。最后，我们使用分类器softmax对高光谱玉米种子图像进行检测和分类。与其他深度学习技术和传统方法相比，ERNet表现出色。其准确率达到98.36%，该结果为包括高光谱玉米种子图片在内的分类研究提供了有价值的参考。
