Zheng Ling, Zhao Mingyue, Zhu Jinchen, Huang Linsheng, Zhao Jinling, Liang Dong, Zhang Dongyan
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China.
Front Plant Sci. 2023 Jan 18;13:1098864. doi: 10.3389/fpls.2022.1098864. eCollection 2022.
Identification of soybean kernel damages is significant to prevent further disoperation. Hyperspectral imaging (HSI) has shown great potential in cereal kernel identification, but its low spatial resolution leads to external feature infidelity and limits the analysis accuracy. In this study, the fusion of HSI and RGB images and improved ShuffleNet were combined to develop an identification method for soybean kernel damages. First, the HSI-RGB fusion network (HRFN) was designed based on super-resolution and spectral modification modules to process the registered HSI and RGB image pairs and generate super-resolution HSI (SR-HSI) images. ShuffleNet improved with convolution optimization and cross-stage partial architecture (ShuffleNet_COCSP) was used to build classification models with the optimal image set of effective wavelengths (OISEW) of SR-HSI images obtained by support vector machine and ShuffleNet. High-quality fusion of HSI and RGB with the obvious spatial promotion and satisfactory spectral conservation was gained by HRFN. ShuffleNet_COCSP and OISEW obtained the optimal recognition performance of ACC=98.36%, Params=0.805 M, and FLOPs=0.097 G, outperforming other classification methods and other types of images. Overall, the proposed method provides an accurate and reliable identification of soybean kernel damages and would be extended to analysis of other quality indicators of various crop kernels.
大豆籽粒损伤的识别对于防止进一步的操作失误具有重要意义。高光谱成像（HSI）在谷物籽粒识别方面已显示出巨大潜力，但其低空间分辨率导致外部特征失真，并限制了分析精度。在本研究中，将高光谱成像与RGB图像融合以及改进的ShuffleNet相结合，开发了一种大豆籽粒损伤识别方法。首先，基于超分辨率和光谱修正模块设计了高光谱成像- RGB融合网络（HRFN），以处理配准后的高光谱成像和RGB图像对，并生成超分辨率高光谱成像（SR-HSI）图像。采用通过卷积优化和跨阶段局部架构改进的ShuffleNet（ShuffleNet_COCSP），利用支持向量机和ShuffleNet获得的SR-HSI图像的有效波长最佳图像集（OISEW）构建分类模型。HRFN实现了高光谱成像和RGB的高质量融合，具有明显的空间提升和令人满意的光谱保持。ShuffleNet_COCSP和OISEW获得了最佳识别性能，ACC = 98.36%，Params = 0.805 M，FLOPs = 0.097 G，优于其他分类方法和其他类型的图像。总体而言，所提出的方法为大豆籽粒损伤提供了准确可靠的识别，并将扩展到各种作物籽粒其他质量指标的分析。
