Wang Xuquan, Ma Zhiyuan, Xing Yujie, Peng Tianfan, Dun Xiong, He Zhuqing, Zhang Jian, Cheng Xinbin
MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai 200092, People's Republic of China.
Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China.
R Soc Open Sci. 2024 Jul 31;11(7):240485. doi: 10.1098/rsos.240485. eCollection 2024 Jul.
Species discrimination of insects is an important aspect of ecology and biodiversity research. The traditional methods based on human visual experience and biochemical analysis cannot strike a balance between accuracy and timeliness. Morphological identification using computer vision and machine learning is expected to solve this problem, but image features have poor accuracy for very similar species and usually require complicated networks that are unfriendly to portable edge devices. In this work, we propose a fast and accurate species discrimination method of similar insects using hyperspectral features and lightweight machine learning algorithm. Feature regions selection, feature spectra selection and model quantification are used for the optimization of discriminating network. The experimental results of six similar butterfly species in the genus of show that, compared with morphological recognition with machine vision, our work achieves a higher accuracy of 92.36 ± 3.04% and a shorter inference time of 0.6 ms, with the tiny-size convolutional neural network deployed on a neural network chip. This study provides a rapid and high-accuracy species discrimination method for insects with high appearance similarity and paves the way for field discriminations using intelligent micro-spectrometer based on on-chip microstructure and artificial intelligence chip.
昆虫的物种鉴别是生态学和生物多样性研究的一个重要方面。基于人类视觉经验和生化分析的传统方法无法在准确性和及时性之间取得平衡。利用计算机视觉和机器学习进行形态学鉴定有望解决这一问题，但对于非常相似的物种，图像特征的准确性较差，并且通常需要复杂的网络，这对便携式边缘设备不太友好。在这项工作中，我们提出了一种利用高光谱特征和轻量级机器学习算法对相似昆虫进行快速准确的物种鉴别方法。特征区域选择、特征光谱选择和模型量化用于鉴别网络的优化。对六种相似蝴蝶属物种的实验结果表明，与机器视觉的形态学识别相比，我们的工作实现了更高的准确率，达到92.36±3.04%，推理时间更短，为0.6毫秒，且在神经网络芯片上部署了微小尺寸的卷积神经网络。本研究为外观相似度高的昆虫提供了一种快速、高精度的物种鉴别方法，并为基于片上微结构和人工智能芯片的智能微型光谱仪进行现场鉴别铺平了道路。
