Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experience of the appraisers or the various measuring instruments, and the methods are either easily influenced by appraisers’ experience or require too much work. To solve the above problems, there are studies using image recognition and intelligent algorithms to identify minerals. However, current studies cannot identify many minerals, and the accuracy is low. To increase the number of identified minerals and accuracy, we propose a method that uses both mineral photo images and the Mohs hardness in deep neural networks to identify the minerals. The experimental results showed that the method can reach 90.6% top-1 accuracy and 99.6% top-5 accuracy for 36 common minerals. An app based on the model was implemented on smartphones with no need for accessing the internet and communication signals. Tested on 73 real mineral samples, the app achieved top-1 accuracy of 89% when the mineral image and hardness are both used and 71.2% when only the mineral image is used.

中文翻译：

---

基于深度学习的结合图像和莫氏硬度的矿物识别

矿物鉴定是地质分析的重要组成部分。传统的识别方法要么依赖于评估人员的经验，要么依赖于各种测量工具，并且这些方法要么容易受到评估人员经验的影响，要么需要过多的工作。为了解决上述问题，进行了使用图像识别和智能算法来识别矿物的研究。但是，目前的研究无法鉴定出许多矿物，并且准确性较低。为了增加识别出的矿物的数量和准确性，我们提出了一种使用矿物照片图像和深度神经网络中的莫氏硬度来识别矿物的方法。实验结果表明，该方法对36种常见矿物质的top-1精度可达90.6％，top-5精度可达99.6％。基于该模型的应用程序已在智能手机上实现，无需访问互联网和通信信号。该应用程序在73种真实的矿物样品上进行了测试，当同时使用矿物图像和硬度时，该应用程序的top-1准确性达到89％，而仅使用矿物图像时达到71.2％。