College of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.
Sensors (Basel). 2024 Oct 8;24(19):6474. doi: 10.3390/s24196474.
As the primary grain crop in China, wheat holds a significant position in the country's agricultural production, circulation, consumption, and various other aspects. However, the presence of imperfect grains has greatly impacted wheat quality and, subsequently, food security. In order to detect perfect wheat grains and six types of imperfect grains, a method for the fast and non-destructive identification of imperfect wheat grains using hyperspectral images was proposed. The main contents and results are as follows: (1) We collected wheat grain hyperspectral data. Seven types of wheat grain samples, each containing 300 grains, were prepared to construct a hyperspectral imaging system for imperfect wheat grains, and visible near-infrared hyperspectral data from 2100 wheat grains were collected. The Savitzky-Golay algorithm was used to analyze the hyperspectral images of wheat grains, selecting 261 dimensional effective hyperspectral datapoints within the range of 420.61-980.43 nm. (2) The Successive Projections Algorithm was used to reduce the dimensions of the 261 dimensional hyperspectral datapoints, selecting 33 dimensional hyperspectral datapoints. Principal Component Analysis was used to extract the optimal spectral wavelengths, specifically selecting hyperspectral images at 647.57 nm, 591.78 nm, and 568.36 nm to establish the dataset. (3) Particle Swarm Optimization was used to optimize the Support Vector Machines model, Convolutional Neural Network model, and MobileNet V2 model, which were established to recognize seven types of wheat grains. The comprehensive recognition rates were 93.71%, 95.14%, and 97.71%, respectively. The results indicate that a larger model with more parameters may not necessarily yield better performance. The research shows that the MobileNet V2 network model exhibits superior recognition efficiency, and the integration of hyperspectral image technology with the classification model can accurately identify imperfect wheat grains.
作为中国的主要粮食作物，小麦在中国的农业生产、流通、消费等各个方面都占有重要地位。然而，不完善粒的存在极大地影响了小麦的品质，进而影响了粮食安全。为了检测出完整的麦粒和六种类型的不完善粒，提出了一种利用高光谱图像快速无损识别不完善粒的方法。主要内容和结果如下：（1）采集小麦籽粒高光谱数据。制备了七种类型的小麦籽粒样本，每个样本包含 300 粒，构建了不完善粒小麦籽粒高光谱成像系统，采集了 2100 粒小麦的可见近红外高光谱数据。采用 Savitzky-Golay 算法对小麦籽粒高光谱图像进行分析，选择 420.61-980.43nm 范围内的 261 维有效高光谱数据点。（2）采用连续投影算法对 261 维高光谱数据点进行降维，选择 33 维高光谱数据点。采用主成分分析提取最优光谱波长，选择 647.57nm、591.78nm 和 568.36nm 的高光谱图像建立数据集。（3）采用粒子群算法优化支持向量机模型、卷积神经网络模型和 MobileNet V2 模型，建立模型识别七种类型的小麦籽粒。综合识别率分别为 93.71%、95.14%和 97.71%。结果表明，参数较多的较大模型不一定能得到更好的性能。研究表明，MobileNet V2 网络模型具有较高的识别效率，高光谱图像技术与分类模型的结合可以实现对不完善粒小麦的准确识别。
