## 4 Future Research and Directions  
SEM is a powerful and resourceful tool that can be employed in various fields for the analysis and characterization of minerals, such as mining [183; 184; 185], oil and gas [186; 187; 188], forensic science [189; 190; 191], biomedical research [192; 193; 194], geology [195; 196; 197], materials science [198; 199; 200], and environmental science [201; 202; 203]. An evolution is being witnessed in mineral processing engineering. Previously, there were extended levels of complexities and practical challenges in managing and optimizing a mineral processing plant, which did not allow for data-based optimization development. Empirical characterization tests were used for designing systems, and operator intuition played a key role in plant operation, which is subjective and varies from case to case. Now, technology is available to make the whole process objective, with the capability to collect, manage, and analyze the retrieved information in large amounts. These technologies, artificial intelligence (AI) and machine learning (ML), possess revolutionizing capacities for designing, managing, operating, and analyzing mineral processing plants [204]. With the advancement of ML and deep learning technologies, the automation of many complex tasks with human execution accuracy is becoming possible, which can replace repetitive and tedious tasks, mitigate subjective human errors, lower analysis costs, and improve the time efficiency of the characterization process. Deep learning methods in microscopic imaging have now been developed to automate mineral grain segmentation and recognition [205; 206; 207]. With recent AI developments, the intelligent identification and quantification of minerals is becoming possible [208; 209; 210]. The voids between geological and artificial intelligence sciences can be filled with the latest research advancements. This can take SEM mineral characterization to another level, with a greater level of objective autonomy and quicker solutions for mineral analysis. The intelligent identification of minerals can be conducted in a generally consistent process, which can be divided into five segments, i.e., mineral dataset acquisition, preprocessing mineral datasets, training the mineral identification models, validating the accuracy of the mineral identification tool, and ensuring the synchronization and integration of the intelligent tool with existing SEM systems. [PERSON] et al. recently used a multiscale dilated convolutional attention network for the rapid identification of minerals with portable Raman spectroscopy [211]. A similar approach can be used for the development of a portable intelligent SEM system. [PERSON] et al., in the recent past, used SEM/EDS data in machine learning applications to automatically classify the heavy minerals in river sand [212]. Likewise, models can be trained to quantify the mineral composition of ones. In other recent work, [PERSON] et al. made use of a deep convolutional neural network by combining mineral image features and hardness data for mineral identification purposes [213]. This investigation was an intuitive way of utilizing deep learning methods for mineral characterization by integrating mineral datasets comprising various properties. Intelligent systems based on a cascade approach for mineral identification in thin sections are already underway for space exploration [214]. It is a matter of extending these methods to SEM systems for more in-depth insight. Future research directions in SEM lead toward the possible incorporation or upgradation of such tools to enhance the effectiveness of SEM as a material characterization tool. This would not only help in reducing the factor of human error by providing objectivity to mineral analysis but also provide cost-effective solutions to the extent where human intervention has never been thought of before.