Core Viewpoint - The article discusses the advancements in Near Infrared Spectroscopy (NIRS) technology, highlighting innovations in hardware, data processing methods, and diverse applications across various industries, indicating a trend towards more intelligent and accessible analytical tools for precision agriculture, green industry, and personalized medicine [1]. Group 1: Innovations in Hardware and Portable Applications - The development of miniaturized, intelligent, and cost-effective NIRS devices has expanded field detection applications, with a focus on balancing portability and performance [3][4]. - Notable examples include a handheld NIRS device developed by an Australian company that integrates MEMS/InGaAs sensor modules, significantly reducing costs while maintaining sensitivity and resolution [3]. - Practical applications of portable devices include food safety assessments, drug testing, and quality control in coffee production, demonstrating their effectiveness in real-world scenarios [5]. Group 2: Integration with Cloud Computing and IoT - The integration of portable NIRS with RFID, blockchain, and IoT has enabled the creation of comprehensive traceability systems, enhancing the digital supply chain [6]. - A New Zealand company successfully replaced 40 online and offline spectrometers with a standardized NIR network, ensuring data consistency throughout the production chain [6]. Group 3: Development of Specialized Spectrometers - Innovations in specialized spectrometers, such as the MiniSmartSensor developed by SINTEF in Norway, allow for precise subsurface detection in food quality analysis [7]. Group 4: Advances in Data Processing and Model Building - The conference highlighted the shift from traditional PLS regression to more adaptive modeling strategies, improving robustness and interpretability in complex sample analysis [9]. - New methodologies, such as the "first principles" approach and data augmentation techniques, have been introduced to enhance model performance and address small sample calibration challenges [9][10]. Group 5: Expansion of Application Scenarios - NIRS technology is increasingly applied across diverse fields, including bioenergy optimization, agricultural quality assessment, and industrial applications, showcasing its cross-industry penetration [18][19]. - Noteworthy applications include real-time monitoring of biogas production and non-destructive quality assessment of organic oranges, demonstrating the versatility of NIRS [18]. Group 6: Automation and Intelligent Applications - The introduction of automation technologies has significantly improved the efficiency of NIRS applications, transitioning from laboratory settings to field and industrial environments [21]. - Examples include collaborative robots for automated wood sample processing and drone systems for real-time vineyard monitoring [23][24]. Group 7: Environmental and Medical Innovations - NIRS technology is favored in environmental monitoring and healthcare due to its green characteristics, enabling efficient detection of microplastics and real-time dialysis monitoring [28][29]. Group 8: Multimodal Data Fusion and Future Prospects - The integration of multimodal data fusion is a key development direction for NIRS, enhancing model accuracy and applicability [36]. - Future advancements are expected to focus on smaller, smarter sensors, the fusion of physical models with data-driven approaches, and the expansion of NIRS applications into complex scenarios [41][42].

"视觉市场目前已经进入瓶颈期，产业竞争聚焦于降低成本。但是从技术上，对特定场景的定制化是不 足的，缺乏成熟设备快速采集高维信号，如三维、高光谱等更高维的数据。"「西湖智能」创始人袁鑫 博士指出。 袁鑫博士是西湖大学工学院感知与计算成像实验室负责人、博士生导师，是国际计算成像领域权威专 家。因其在单曝光压缩计算成像（Snapshot Compressive Imaging）领域的开创性贡献，于2025年入选美 国光学学会会士（Optica Fellow）。他同时是国家海外高层次青年人才、浙江省高层次人才、浙江省杰 青获得者，并入选斯坦福全球前2%顶尖科学家"终身科学影响力排行榜"。袁博士拥有香港理工大学博 士学位、美国杜克大学博士后经历，并曾担任美国贝尔实验室视频分析与编码首席研究员。截至目前， 其发表学术论文近300篇，谷歌学术引用超13000次，H指数59；申请专利30余项（已授权15项），多项 专利已成功实现产业转化；并担任多个顶级国际期刊编委。 文｜本子 编辑｜廖尧 36氪广东获悉，国内计算成像领域领军企业、高光谱成像设备创新者西湖智能视觉科技（杭州）有限公 司（下称「西湖智能」）近日宣布完成超五千万元 ...

Core Insights - KoBold Metals is revolutionizing mineral resource exploration using artificial intelligence, making the process more efficient, precise, and scalable [2][3] - The company invests over $100 million annually across 70 projects on four continents, focusing on metals essential for batteries and AI-driven businesses, such as lithium and copper [3][4] Group 1: Company Operations - KoBold's operations focus on mineral exploration rather than mining, as exploration offers greater value creation and economic benefits [5][6] - The company employs a comprehensive technology stack that includes sensors for data collection, data systems for integration, and predictive models to enhance exploration capabilities [4][6][15] - KoBold's exploration projects are primarily in North America, Europe, Australia, and Africa, with a strong emphasis on copper, lithium, and nickel [12][13] Group 2: Data Utilization - The company utilizes a vast array of geological data, including satellite imagery, airborne surveys, and historical geological reports, to identify potential mineral deposits [7][8][10] - KoBold combines structured and unstructured data to improve predictive modeling, allowing for better decision-making in exploration [15][19] - The integration of various data types enhances the company's ability to make informed predictions about mineral locations [19][20] Group 3: Economic Viability - The economic viability of a mining project is determined by its mineral concentration and scale, with high-grade deposits offering significantly lower operational costs [14][21] - KoBold aims to control the average discovery cost per successful project between $50 million and $100 million, which is a competitive target in the industry [22][23] - The company believes that the real scarcity lies in high-quality mineral deposits rather than funding, as good projects always attract buyers [23][24] Group 4: Industry Challenges - The exploration success rate in the industry has decreased significantly over the past 30 years, with current estimates suggesting a success rate of around 0.5% [21][22] - Regulatory challenges and community relations are critical factors in the feasibility of mining projects, requiring companies to establish strong local partnerships [11][12] - Despite the challenges, there are still underexplored regions with significant potential for mineral discovery, particularly in areas like Zambia and the Democratic Republic of the Congo [24][25] Group 5: Philosophical Approach - KoBold emphasizes a philosophical approach to exploration, focusing on making clear, falsifiable predictions and embracing uncertainty in data collection [34][35] - The company's exploration philosophy includes maintaining multiple hypotheses and using data to reduce uncertainty, which is central to its operational strategy [35][36] - The integration of philosophical thinking into business practices helps guide decision-making and enhances the company's predictive capabilities [36][37]