Zhang Fu, Zhang Fangyuan, Wang Shunqing, Li Lantao, Lv Qiang, Fu Sanling, Wang Xinyue, Lv Qingfeng, Zhang Yakun
College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China.
Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang, China.
Front Plant Sci. 2023 Sep 8;14:1254548. doi: 10.3389/fpls.2023.1254548. eCollection 2023.
As the third largest food crop in the world, maize has wide varieties with similar appearances, which makes identification difficult. To solve the problem of identification of hybrid maize varieties, a method based on hyperspectral image technology combined with a convolutional neural network (CNN) is proposed to identify maize varieties.
In this study, 735 maize seeds from seven half-parent hybrid maize varieties were regarded as the research object. The maize seed images in the range of 900 ~ 1700nm were obtained by hyperspectral image acquisition system. The region of interest (ROI) of the embryo surface was selected, and the spectral reflectance of maize seeds was extracted. After Savitzky-Golay (SG) Smoothing pretreatment, Maximum Normalization (MN) pretreatment was performed. The 56 feature wavelengths were selected by Competitive Adaptive Reweighting Algorithm (CARS) and Successive Projection Algorithm (SPA). And the 56 wavelengths were mapped to high-dimensional space by high-dimensional feature mapping and then reconstructed into three-dimensional image features. A five-layer convolution neural network was used to identify three-dimensional image features, and nine (SG+MN)-(CARS+SPA)-CNN maize variety identification models were established by changing the input feature dimension and the depth factor size of the model layer.
The results show that the maize variety classification model works best, when the input feature dimension is 768 and the layer depth factor d is 1.0. At this point, the model accuracy of the test set is 96.65% and the detection frame rate is1000 Fps/s in GPU environment, which can realize the rapid and effective non-destructive detection of maize varieties. This study provides a new idea for the rapid and accurate identification of maize seeds and seeds of other crops.
玉米作为世界第三大粮食作物，品种繁多且外观相似，难以鉴别。为解决杂交玉米品种的鉴别问题，提出一种基于高光谱图像技术结合卷积神经网络（CNN）的玉米品种鉴别方法。
本研究以7个半亲本品系杂交玉米品种的735粒玉米种子为研究对象。通过高光谱图像采集系统获取900~1700nm范围内的玉米种子图像。选取胚表面的感兴趣区域（ROI），提取玉米种子的光谱反射率。经过Savitzky-Golay（SG）平滑预处理后，进行最大归一化（MN）预处理。采用竞争性自适应重加权算法（CARS）和连续投影算法（SPA）选取56个特征波长。并将这56个波长通过高维特征映射映射到高维空间，然后重构为三维图像特征。使用五层卷积神经网络对三维图像特征进行鉴别，通过改变模型层的输入特征维度和深度因子大小，建立了9个（SG+MN）-（CARS+SPA）-CNN玉米品种鉴别模型。
结果表明，当输入特征维度为768且层深度因子d为1.0时，玉米品种分类模型效果最佳。此时，在GPU环境下测试集的模型准确率为96.65%，检测帧率为1000 Fps/s，能够实现对玉米品种的快速有效无损检测。本研究为玉米种子及其他作物种子的快速准确鉴别提供了新思路。
