This study proposes an innovative deep learning model for use in a multispectral vision system comprising a complementary metal-oxide semiconductor image sensor and a spectrometer. To ensure accurate color recognition, the deep learning model includes an embedded adaptive automatic color temperature correction engine. By using this color temperature correction engine, the multispectral vision system can intelligently compensate for lighting and chromatic variations. To evaluate the performance of the system, we created a nine-dimensional data set using the IT8.7/2 color target. We then used this data set to train the deep learning model. Our deep learning model outperformed other lightweight deep learning models in experiments, making it suitable for deployment on edge devices and embedded systems. We tested the ability of the multispectral vision system to classify adulterated coffee beans into their respective classes. The overall accuracy rate was more than 99.3%, indicating that out proposed multispectral vision system is effective in identifying color differences. Considering its capabilities in agricultural screening, we suggest incorporating our adaptive automatic multispectral vision system into agricultural machines for the realization of Agriculture 4.0.

中文翻译：

---

使用智能多光谱视觉系统对不同咖啡烘焙程度进行智能分类

这项研究提出了一种创新的深度学习模型，用于由互补金属氧化物半导体图像传感器和光谱仪组成的多光谱视觉系统。为了确保准确的颜色识别，深度学习模型包含嵌入式自适应自动色温校正引擎。通过使用这种色温校正引擎，多光谱视觉系统可以智能地补偿照明和色度变化。为了评估系统的性能，我们使用 IT8.7/2 颜色目标创建了一个九维数据集。然后我们使用这个数据集来训练深度学习模型。我们的深度学习模型在实验中优于其他轻量级深度学习模型，使其适合部署在边缘设备和嵌入式系统上。我们测试了多光谱视觉系统将掺假咖啡豆分类为各自类别的能力。总体准确率超过99.3%，表明所提出的多光谱视觉系统能够有效识别色差。考虑到其在农业筛选方面的能力，我们建议将我们的自适应自动多光谱视觉系统融入农业机械中，以实现农业4.0。