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Last Updated: Apr 25, 2025 | Study Period: 2023-2030
Automotive 3D depth sensors are crucial to the monitoring of systems inside a car, enabling cutting-edge car cockpits, seamless connection, and enhanced passive safety.As drivers become more similar to passengers, these sensors are becoming crucial for adhering to rules and NCAP ratings.
The distortion and intensity of the pattern are then detected by a 3D sensing camera to ascertain the object's relative distance and shape. The 3D surface is rebuilt using a computer algorithm.
The Global Automotive 3D Image Sensor market accounted for $XX Billion in 2022 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2023 to 2030.
The first automotive ISO26262-compliant 3D image sensor was launched by Infineon. They are providing high resolution with a small image circle to the automotive industry by leveraging their leadership in 3D sensors for consumer applications. This enables consumer-level features in cars while upholding automotive norms and even enhancing passive safety.
For instance, trustworthy and secure facial authentication enables smooth connectivity for any service that needs authentication, such as payment, battery charging, or accessing personal information.The sensor has a small picture circle of 4 mm and a VGA system resolution of 640 x 480 pixels. It is packaged in a 9 Ã 9 mm2 plastic BGA box.
This makes it possible to use smartphone-like lens diameters for automotive applications as well.Due to the REAL3 sensor's excellent resolution, it can also be used for camera applications that require a large field of view, like entire front-row passenger monitoring systems.
Insightful airbag deployment and restraint systems require accurate estimations of occupant size and weight, as well as very precise data on passenger and seat position, which are made possible by the resulting 3D body models.
In addition to being compact, the single-chip solution is the first of its kind, certified to AEC-Q100 Grade 2, and was created in accordance with the ISO26262 (ASIL-B) standard. The 3D data enables comfort features like gesture control or intelligent interior illumination that adapts to the movements of the passengers.
Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing. Plant phenomics is a young scientific area that has gained attention in the scientific world recently as a result of technology advancements. The quantitative description of a plant's physiological and biochemical characteristics is known as plant phenotyping.
Plant phenotyping has historically mostly depended on the visual scoring of experts, which is highly time-consuming and labor-intensive and can lead to bias between various experts and repetitions of the experiment.
Destructive measurements of plant tissue are necessary for more precise trait determinations. There are several ways to phenotype plants, including using satellites, aeroplanes, ground-based vehicles, and controlled settings.
High-throughput plant phenotyping is the process of quickly, non-destructively, and accurately detecting and measuring plant attributes on a regular basis. By effectively measuring essential plant characteristics like photosynthesis, drought resistance, blooming biomass, and nutrition, high-throughput plant phenotyping helps expedite the process of choosing the next generation of climate change-tolerant, sustainable crops.
Many plant phenotyping techniques, such as thermal imaging, fluorescence techniques, computed tomography, magnetic resonance imaging, positron emission tomography, non-imaging spectrometers, HSI, and 3D sensing, have been developed in the last 20 years.
Modern plant phenotyping methods have been thoroughly reviewed from a variety of angles in a number of outstanding reviews. examined high-throughput methods for shoot imaging in order to investigate how plants react to drought.
Imaging methods and their uses in phenotyping plants. described the four characteristics of temperature-related features, morphological features, root systems, and spectrum reflectance that make up image-based plant phenotyping techniques.
Despite the benefits of these reviews, there doesn't seem to be as much literature outlining the specifics of HSI and 3D sensing for plant phenotyping, including sensors, platforms, system setup, calibration, and data processing.
These are two widely used and rapidly evolving methods. Limited publications also explain the connection between 3D and hyperspectral data. Although technology is developing quickly, HSI and 3D technologies are still in their infancy for plant phenotyping, and researchers are encountering several obstacles in their attempts to effectively employ these tools.
The difficulties are in comprehending the demands of data collection, processing, and phenotyping jobs. Depending on the surroundings, crops, characteristics that must be assessed, developmental stages, and resources available, different plant phenotyping needs may apply.
Plant phenotyping may be applied in a range of contexts and at a range of sizes, from the molecular to the canopy, which opens up new possibilities for data collection and processing techniques.
Understanding the fundamentals of light-plant interactions, optical sensors, plant phenotyping platforms, and data processing pipelines is crucial to choosing the right sensors, setting up effective data acquisition platforms, and implementing reliable data processing algorithms to meet plant phenotyping requirements. Users will be able to assess plant phenotyping's kinds, quality, effectiveness, and cost with the use of this knowledge.
This seeks to highlight the existing limits of plant phenotyping utilising HSI and 3D sensing and offer prospective solutions, as well as to provide a basic guideline for setting up HSI- and 3D-based plant phenotyping systems. It begins by outlining how light interacts with popular hyperspectral sensors, lighting, and plants. After that, it offers a thorough analysis of HSI for close-range to satellite plant phenotyping.
| Sl no | Topic |
| 1 | Market Segmentation |
| 2 | Scope of the report |
| 3 | Abbreviations |
| 4 | Research Methodology |
| 5 | Executive Summary |
| 6 | Introduction |
| 7 | Insights from Industry stakeholders |
| 8 | Cost breakdown of Product by sub-components and average profit margin |
| 9 | Disruptive innovation in the Industry |
| 10 | Technology trends in the Industry |
| 11 | Consumer trends in the industry |
| 12 | Recent Production Milestones |
| 13 | Component Manufacturing in US, EU and China |
| 14 | COVID-19 impact on overall market |
| 15 | COVID-19 impact on Production of components |
| 16 | COVID-19 impact on Point of sale |
| 17 | Market Segmentation, Dynamics and Forecast by Geography, 2023-2030 |
| 18 | Market Segmentation, Dynamics and Forecast by Product Type, 2023-2030 |
| 19 | Market Segmentation, Dynamics and Forecast by Application, 2023-2030 |
| 20 | Market Segmentation, Dynamics and Forecast by End use, 2023-2030 |
| 21 | Product installation rate by OEM, 2023 |
| 22 | Incline/Decline in Average B-2-B selling price in past 5 years |
| 23 | Competition from substitute products |
| 24 | Gross margin and average profitability of suppliers |
| 25 | New product development in past 12 months |
| 26 | M&A in past 12 months |
| 27 | Growth strategy of leading players |
| 28 | Market share of vendors, 2023 |
| 29 | Company Profiles |
| 30 | Unmet needs and opportunity for new suppliers |
| 31 | Conclusion |
| 32 | Appendix |
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