This guide explores the fundamentals of image annotation, its techniques, real-world applications, how to choose the right image annotation service provider, and more.
What is Image Annotation?
Image annotation (a subset of data annotation) is labeling images or tagging relevant information, strategically incorporating human-powered efforts and sometimes computer assistance. Labeling images is crucial to build computer vision models for tasks like image classification, image segmentation, and object detection. Labeled images help identify and highlight specific features, such as objects or regions within them, and it can range from the task of annotating a group of pixels to one label for the entire image. Image annotation is also called a key driver of growth truth data, empowering AI and ML models to recognize patterns and make thoughtful decisions on the basis of visual inputs.
What are the Steps of Image Annotation?
The image annotation process involves several key steps:
Image Collection – A dataset of relevant images or videos is gathered such as traffic scenes, medical scans, retail shelves, satellite imagery, etc., as per the AI use case.
Define Label Types – Define label types, involving actions (e.g., walking, waving), objects (e.g., vehicles, tools), or attributes (e.g., color, ripeness).
Create Annotation Classes and Objectives – Project stakeholder define what has to be annotated, including the type of labeling required (e.g., bounding boxes, segmentation), the objects of interest (e.g., people, products, animals), and the context (e.g., behavior, pose, condition).
Trained Annotators – There is a need for skilled human annotators who understand annotation guidelines and objectives.
Right Annotation Tools – After setting label types, annotators use tools such as CVAT, V7, Labelbox, and SuperAnnotate to apply techniques like polygons, keypoints, or bounding boxes. It enables precise and scalable annotations to help computer vision models interpret visual data accurately.
Quality Assurance – Strong QA is key to build reliable and real-world-ready AI models. It involves ensuring annotation accuracy with manual reviews, automated error checks, and expert validation.
Versioning and Export – Maintain version control of annotated datasets and export them in formats compatible with ML models. Formats include JSON, Pickle, or XML as per the usage. The formats could be XML, JSON, or pickle, depending on its intended use. Preferable formats for deep learning models are COCO and Pascal VOC. All such formats support seamless integration with model architectures, built to accept them that reduce the need for extra preprocessing.
What are the Different Techniques of Image Annotation?
There are several types of image annotation, each fitted to specific tasks and precision levels:
Image Classification
Image classification is defined as the simplest form of image annotation, where a single label is assigned to an entire image based on its overall content. Rather than identifying individual objects, the image is classified into a predefined class that presents its dominant subject or theme. This method works best for broad classification tasks where the focus remains on the general context.
Example: An image showing a forest with dense trees, wildlife, and greenery might be classified as a “forest or nature” landscape.”
Object Detection
Object detection is identifying and locating specific objects within an image by placing a bounding box around them and allocating appropriate class labels. Different from image classification, this technique defines what objects are present and specifies their exact position within an image. Bounding boxes typically use rectangles to highlight each object, which is then tagged with its corresponding label.
Example: Bounding boxes, in the image of a kitchen, may be drawn around a microwave, refrigerator, and utensils, with each labeled accordingly (e.g., “microwave,” “fridge,” “spoon”).
Semantic Segmentation
Semantic segmentation involves labeling every pixel in an image to identify the region or object it represents. The technique classifies each pixel to offer a high level of detail that results in a segmented image where distinct regions are defined clearly according to their category. It is perfect for applications that require precise object boundaries and spatial understanding.
Example: In an aerial image of a city, pixels representing roads are labeled “road,” buildings as “building,” and vegetation as “trees” or “greenery.”
Instance Segmentation
Instance segmentation involves assigning a unique label to each individual occurrence of an object within an image while classifying each pixel it occupies. This technique helps identify object classes at the pixel level and distinguishes between various instances of the same class. It is useful for complex or crowded scenes where objects of the same type appear multiple times.
Example: In an image of a fruit basket, each apple is segmented and labeled individually (e.g., “apple 1,” “apple 2”), allowing the model to differentiate between separate apples even though they belong to the same class.
Panoptic Segmentation
Panoptic segmentation combines semantic and instance segmentation strengths by assigning a class label to each pixel in an image and uniquely identifying each object instance where applicable. It provides a complete understanding of the visual scene by segmenting both “things” (countable objects like people or cars) and “stuff” (uncountable regions like sky, road, or grass) in a unified manner. It is a useful technique, especially in applications that require holistic scene interpretation.
Example: In a street scene, panoptic segmentation labels every car and pedestrian as individual instances (e.g., “car 1,” “car 2,” “pedestrian 1”) while also classifying the road, buildings, and sky as distinct background regions.
Types Used in Image Annotation
Image annotation uses various methods to mark visual data depending on the complexity and goals of the project. Some methods utilized include:
Bounding Boxes
Bounding box annotations as per its name require specific objects in an image to be covered by a bounding box. Generally, these annotations are recommended for object detection algorithms, where the box depicts the object boundaries, and does not require precise annotations like segmentation or polygonal. However, it meets the precision required in detector use cases. It is often used to train algorithms for self-driving cars and intelligent video analytics mechanisms.
Polygons
Polygon masks offer more precision than bounding boxes by outlining objects using varied vertices instead of four corners. This helps deliver a more accurate representation of complex shapes while keeping data lightweight and easily vectorized. Polygon annotations balance efficiency and accuracy, making them ideal for training object detection and semantic segmentation models. It is commonly used in fields like natural scene text recognition and medical imaging, where detailed object boundaries are essential.
Polylines
Polyline annotation involves drawing a series of connected lines across an image to mark object boundaries. It is used for tasks that demand line-based predictions, such as lane detection in autonomous driving. With high-precision boundary information, polyline annotation supports train models detecting lanes accurately and identifying drivable areas, allowing self-driving vehicles to navigate roads safely and effectively.
Keypoint / Landmark
Landmark or keypoint annotation involves marking specific coordinates on an image to indicate the location of crucial structures or features. These annotations are commonly used in facial analysis to recognize features like mouth, nose, eyes, and pose estimation to identify body joints for activity recognition. Apart from facial datasets, landmarks or keypoints are also applied in human pose detection, object counting, and gesture recognition for similar items within a scene. Tools like V7 deliver pre-defined skeleton templates, enabling users to quickly place and align landmarks by overlaying structure shapes into the image.
3D Cuboid
3D cuboid annotation extends traditional object detection into three dimensions, helping models to comprehend volume, depth, and orientation, accurately perceiving and interacting with objects in a three-dimensional environment. This technique is especially useful in fields such as medical imaging (e.g., CT or MRI scans) where spatial context is critical.
Pixel-Level Annotation
Pixel-level annotation targets identifying specific areas, applied in segmentation. It produces a detailed mask or silhouette that outlines an object from its background. Unlike polygons or bounding boxes, masks deliver pixel-level exactness, which is perfect for applications demanding high precision, including semantic segmentation, instance segmentation, and medical imaging. This annotation enables AI systems to understand fine-grained borders, address overlapping objects, and discern fine visual differences—critical in applications such as agriculture, autonomous vehicles, and health.
Where to Build Quality Image Data?
The creation of relevant, precise, and accurate image data is no small feat as high-quality datasets are the fuel of training AI models. Considering the specific domain and complexity of a project, the following methods are used for image datasets:
Public Datasets
Public datasets, also known for their open-source nature, are suitable for tasks like model training, benchmarking, and academic research. Open AI communities and research institutions primarily label and maintain open-source datasets.
Examples
- ImageNet is a suitable choice for general object classification tasks.
- COCO (Common Objects in Context) is the best fit for object detection, segmentation, and captioning.
- OpenImages is a dataset laced with object bounding boxes and image-level labels.
- LUNA16 is a medical dataset for lung nodule detection.
- Cityscapes has been curated for urban scene understanding.
Public datasets are the most suitable for experimentation and prototyping, but may lack domain-specific relevance or required granularity for specialized tasks.
Custom Data Collection
Collecting your data for highly specific or proprietary use cases ensures complete control over quality, diversity, and context.
Benefits
- These can be customized for unique environments, products, or conditions (e.g., unusual lighting or rare object classes).
- Captures real-world scenarios aligned with the final application.
- Enables consistent labeling protocols and data structure.
Custom data collection is imperative for agriculture, healthcare, autonomous vehicles, and retail industries, where public data is not meant to depict real-world deployment conditions.
Data Providers
The last ones are leading data providers, who deliver curated, annotated,
and ready-to-use datasets. Image data by data providers are customized for commercial or enterprise-grade AI projects.
Salient Features
Data providers render access to high-precision and large-scale datasets across different verticals. The list incorporates geospatial analysis, medical imaging, e-commerce, and manufacturing.
These are compliance-ready with data privacy standards like HIPAA, GDPR, or etc.
These datasets are powered by services for data collection, cleaning, annotation, and formatting.
Leading data providers:
- Cogito Tech is recognized for delivering high-precision and domain-specific annotated datasets.
- Scale AI, iMerit, and Lionbridge AI offer scalable data annotation and delivery solutions.
- Datatang, Appen, Figure Eight provide multilingual and cross-domain datasets.
How are Companies Handling Image Annotation?
The demand for image annotation is mushrooming to train machine learning models. To efficiently manage image annotation requirements, companies adopt a mix of outsourced annotation partners, in-house teams, and AI-driven tools. The selected approach usually depends on domain sensitivity, data volume, and project complexity.
In-House Annotation
Some companies opt for constructing an in-house team as it offers various advantages such as smooth iteration, full control, and robust data security. In-house approach is preferred by companies working in sensitive domains, including finance, defense, or healthcare, where data confidentiality and compliance remains critical. However, it also comes with significant challenges such as setting up proper training, dedicated staff, and investment in annotation tools. Initially, new annotators in the team may commit mistakes, impacting data quality. In the quest of outpacing team growth, scaling can also appear a bottleneck for the business.
Crowdsourcing
Crowdsourcing distributes annotation tasks into small batches managed by a large pool of contributors, making it a highly cost-effective option. If instructions are clear, it minimizes systematic errors, and is ideal for simple, and high-volume tasks. However, crowdsourced workers often lack domain expertise that make them unsuitable for sensitive datasets such as technical components or medical scans, increasing the need for extensive quality checks. Companies often use a layered review process to sustain quality in crowdsourced data.
Outsourcing
Outsourcing image annotation to a trusted service provider seems a practical option to scale AI development. A specialized service provider promises to deliver solid infrastructure, skilled annotators, and domain expertise, supporting them to tackle large data volumes efficiently across industries like retail, automotive, and medical imaging. The team of annotators also tackles quality control, freeing the internal team to work on core product development dedicatedly. This approach allows you to embrace a balanced approach, uniting the cost-effectiveness and scalability of crowdsourcing with the data security and authenticity of an in-house team. It may mark down flexibility and demand more coordination for changes, but it visibly reduces the internal resource burden at the same time maintaining high-quality annotations. Outsourcing is a cost-effective approach and allows companies to focus internal resources on core AI development rather than data preparation.
Features to Look for in Image Annotation Service Providers
Numerous factors are crucial while selecting the best image annotation company. Let’s evaluate the following:
- Quality and Accuracy
Precise annotations are very essential in AI model generalization and performance. Seek out those annotation businesses that maintain rigorous QA processes, frequent accuracy checkpoints, and multiple levels of reviews. - Annotation Capabilities
You need to assess the depth and breadth of annotation solutions provided. This includes backing for semantic segmentation, bounding boxes, keypoints, polygons, sentiment analysis, 3D point cloud labeling, named entity recognition (NER), and more. An adaptable annotation provider can evolve with your continually changing AI pipeline needs. - Tools and Technology
Have a service provider by your side who leverage annotation platforms – either third-party or proprietary with support for real-time collaboration, integrated QA checks, automation-assisted labeling, and data versioning. Tech-driven workflows boost traceability, efficiency, and consistency across batches. - Scalability
Select a partner that can scale operations and resources according to the size and timeline of your project. Whether you have a small proof-of-concept or large-scale production deployment, the company must have infrastructure, workforce, and project management capacity to tackle dynamic workloads with minimal delays. - Data Compliance & Security
Security should remain prime priority specially dealing with regulated or sensitive data (e.g., financial, personal, medical). Make sure that you find a service provider who adheres to industry standards like GDPR, HIPAA, or ISO 27001. Check with them regarding the use of access controls, secure cloud environments, encryption protocols, and NDAs with annotators. - Customization
You also need to check with the service provider for customization as the project often needs domain-oriented ontologies, custom workflows, or special handling instructions. The selected service provider must provide customization options from platform configuration and annotation guidelines to feedback loops and reporting formats. It helps align with your model training objectives. - Domain Expertise
Domain knowledge is critical for annotations of verticals like finance, autonomous driving, e-commerce, healthcare, and agriculture. A company having experience in your vertical can better comprehend edge cases, terminology, and context, ultimately enhancing relevance and model outcomes. - Turnaround Time
In AI development, time-to-market is critical. You need to assess the ability of your service provider to meet aggressive deadlines without compromising quality. Experience in handling tight delivery cycles, agile workforce allocation, and transparent timeline are strong indicators of operational maturity. - Cost-Effectiveness
Price must not be the sole deciding factor. Deciding factors, flexible and competitive pricing models, subscription-based, or hourly per annotation can provide better value as per complexity and size of the project. Look for no hidden fees, pricing transparency, and the ability to scale cost-efficiently as data volume expands. - Customer Support
Robust client support can create a huge impact during onboarding, iterations, and execution. Select a service partner with clear communication practices, experienced support staff, and responsible account managers. Frequent check-ins, escalation paths, and progress reports showcase a commitment to long-term partnership.
Common Image Annotation Use Cases
Image annotation has become an integral part of training computer vision systems across industries. By labeling visual data with precision, it empowers AI to see, interpret, and act in real-world environments.
Face Recognition
Annotated facial features train models to verify identities for secure access, unlocking devices, and crowd analytics.
Security and Surveillance
Helps detect suspicious activities, intrusions, or unusual behavior by labeling people, objects, or motion patterns in video feeds.
AgriTech (Agricultural Technology)
Annotating crop images allows AI to assess plant health, detect diseases, and predict yields with precision farming tools.
Medical Imaging
Precise annotations of X-rays, MRIs, and CT scans assist in identifying tumors, anomalies, and disease progression, improving diagnostic accuracy.
Robotics
Enables robots to interpret visual data for navigation, object manipulation, and human interaction in industrial or domestic environments.
Autonomous Vehicles
Trains self-driving systems to detect traffic signs, lane markings, pedestrians, and other vehicles for real-time decision-making.
Drone and Aerial Imagery
Supports land surveying, infrastructure monitoring, and disaster assessment by labeling terrain, structures, and environmental changes.
Insurance
Speeds up claim processing by using annotated images to assess property or vehicle damage, enhancing fraud detection and documentation.
High‑Performance Image Annotation Tools at Cogito Tech
Cogito Tech employs tools to deliver precision-driven, scalable image annotation across industries. This is backed by rigorous quality control and domain-specific tuning.
Key Tools & Technologies
- Tools for Complex and High-Volume Annotations
Cogito Tech partners with tools like Labelbox, SuperAnnotate, V7 Darwin, etc., high-volume and complex annotation workflows. It supports multiple annotation types—bounding boxes, polygons, semantic segmentation, 3D cuboids, keypoints, and LiDAR point clouds—ensuring accurate labeling across diverse domains like autonomous vehicles, healthcare, and robotics. Integrated QA layers and support for custom ontology make it ideal for production-grade AI development.
Partner Tools
- Labelbox for collaborative image annotation and pipeline automation.
- SuperAnnotate for scalable computer vision annotation with QA flows.
- V7 Darwin for intelligent workflows and advanced image/video labeling.
- Dataloop for continuous AI training with auto-labeling features and dev-friendly APIs.
- RedBrick AI is a powerful web-based tool for annotating medical images such as CT, MRI, and ultrasound scans. Its platform determines high-quality standards through customizable quality control workflows.
- CVAT (Computer Vision Annotation Tool)
The open-source Intel-built platform is used for object detection, interpolation across video frames, and semi-automatic deep‑learning-assisted labeling. It is especially suitable for temporal and motion-sensitive data. - Tool Integrations (Labelbox, V7, or others as needed)
Cogito Tech leverages modular platforms like Labelbox and V7 to support custom training loops, model-assisted annotation, and collaborative feedback workflows for clients requiring advanced modeling integrations or analytics dashboards.
Factors Influencing Pricing of Image Annotation for AI/ML Projects
Estimating Image Annotation Pricing for AI/ML Project
Several factors influence a range of project-specific factors. Understanding these supports team budget accurately and escape surprise pricing
- Volume of Data: Large datasets often benefit from economies of scale. Vendors may offer discounts when dealing with hundreds of thousands or millions of images, especially when annotation types are standardized.
- Type of Annotation: The complexity of annotation methods directly impacts pricing. Simple tasks such as image classification or bounding boxes are more affordable, while intricate techniques, including polygonal segmentation, 3D cuboids, or LiDAR annotations are resource-intensive and therefore costlier.
- Image Complexity: Highly detailed images—such as medical scans, aerial drone shots, or crowded retail scenes—require more time, expert interpretation, and precision, which increases annotation costs.
- Quality & Accuracy Requirements: When annotations must meet strict quality standards—especially in regulated industries like healthcare or autonomous driving—extra validation layers (like QA reviews or consensus labeling) are necessary, raising the cost per image.
- Project Urgency: Fast-track projects may require additional human or system resources to meet compressed deadlines, adding to overall pricing through rush fees or overtime labor.
- Tooling & Integration Needs: If the project requires use of specific tools, APIs, or a custom ontology setup, setup time and system integration may also contribute to the total project cost.
Image Annotation Using Cogito Tech
Cogito Tech is a premier image annotation service provider that combines skilled human annotators with cutting-edge technologies to deliver high-quality, domain-specific training data. Here’s a deeper look into what sets them apart
Expert Annotators: Cogito Tech workforce incorporates trained professionals with experience handling complex data across multiple domains, ensuring consistent accuracy and reduced error rates even in edge cases.
Advanced Annotation Tools: Leveraging proprietary platforms and partner tools like CVAT, Label Studio, V7, and SuperAnnotate, Cogito Tech enables faster turnaround with features like QA integration, automation-assisted annotation, and ontology customization.
Scalable Solutions: Whether a pilot project or large enterprise deployment, Cogito Tech provides agile scaling capabilities, quickly ramping up workforce and tools to meet client timelines and data volume requirements.
Industry-Specific Expertise: Having experience in sectors like autonomous vehicles, healthcare, agriculture, robotics, and e-commerce, Cogito Tech tailors workflows and taxonomies to meet unique project demands.
Data Security & Compliance: Holding certifications like GDPR, HIPAA, and ISO, Cogito Tech determines strict compliance with global data privacy standards, delivering secure infrastructure and confidentiality protocols for sensitive projects.
Top 10 Image Annotation & Labeling Service Providers in 2025
Check out the top ten image annotation companies redefining computer vision and other AI models with high-quality, scalable image annotation solutions. These providers enable AI teams to train accurate, real-world-ready models across numerous industries.
Cogito Tech
In 2025, Cogito Tech will be a premier image annotation and data labeling service provider trusted by global enterprises and AI startups alike. With over a decade of experience in human-in-the-loop data solutions, Cogito Tech delivers high-quality, scalable, and domain-specific annotations that power the most advanced computer vision models.
What Sets Cogito Tech Apart?
- Cogito Tech delivers diverse annotation types—bounding boxes, polygons, segmentation, 3D cuboids, LiDAR, and more—tailored to industries like robotics, healthcare, and autonomous driving.
- Blending expert annotators with AI tools ensures high accuracy, faster turnaround, and minimal rework.
- Trained teams handle complex tasks, from detecting manufacturing defects to labeling medical images.
- HIPAA, GDPR, and ISO-certified processes ensure data privacy and regulatory compliance.
- Supports projects of any size with agile teams, custom workflows, and adaptable ontology.
Anolytics
In 2025, Anolytics is recognized as a leading image annotation and data labeling company, delivering scalable, cost-effective, and precise solutions to power real-world AI applications across diverse industries.
What Sets Anolytics Apart:
- Strikes a balance between quality and cost-efficiency, making it ideal for large-volume annotation projects without compromising accuracy.
- Supports a broad range of computer vision applications—from aerial imagery and autonomous driving to e-commerce, healthcare, and industrial automation.
- Leverages proprietary platforms and third-party tools with automation accelerators and QA layers to ensure faster turnaround and consistent quality.
- Maintains a flexible, trained workforce that adapts quickly to changing guidelines, handles edge cases, and meets evolving project demands with agility.
Labellerr
Labellerr is a popular image annotation company offering AI-powered labeling solutions to accelerate computer vision development with scalability and efficiency.
Top Characteristics
Delivers support to a wide range of annotation types: bounding boxes, polygons, segmentation, object tracking, and more.
Industry-specific solutions for autonomous vehicles, agriculture, healthcare, and retail.
AI-assisted tools united with human-in-the-loop workflows for enhanced accuracy.
Scalable platform designed to handle large, complex datasets.
Cost-efficient annotation process with quick turnaround times.
Intuitive interface and project management capabilities for easy collaboration.
Scale AI
Scale AI is a premier provider of scalable image annotation and 3D labeling solutions for modern AI applications. Trusted by leading tech and autonomous vehicle companies, it amalgamates automation with human expertise for precision at scale.
What differentiates ScaleAI?
Image annotation for segmentation, object detection, and classification.
Scalable platform with automation-assisted labeling
3D LiDAR annotation and sensor fusion for autonomous systems.
Human-in-the-loop workflows for better accuracy.
Fast, production-ready datasets for machine learning models.
CloudFactory
CloudFactory provides scalable image annotation and data labeling services by blending skilled human workers with cloud technology. Trusted by global companies, it delivers high-quality training data for AI across diverse industries.
Salient Features
Image annotation for object detection, segmentation, and classification.
Data labeling for computer vision, NLP, and audio tasks.
Scalable workforce to handle high-volume, complex projects.
Industry coverage: automotive, retail, agriculture, healthcare, and more.
Secure, cloud-based workflows with strong QA processes.
Ethical sourcing and workforce development are built into the model.
Amazon Mechanical Turk
MTurk is a recognized crowdsourcing platform, connecting businesses with a global, on-demand workforce to complete microtasks like data annotation and image labeling at scale. It is widely used for fast, cost-effective AI and machine learning dataset creation.
Prime Capabilities
Supports tasks like image classification, object tagging, and text annotation.
Ideal for high-volume, repetitive data labeling needs.
Offers quick turnaround with thousands of remote workers.
Highly cost-efficient for early-stage and experimental projects.
Requires strong quality control measures and validation workflows.
Flexible task design for diverse annotation formats.
iMerit
iMerit provides high-quality data annotation services powered by a skilled, in-house workforce. This enables AI companies to build accurate, responsible, and inclusive AI models across industries. Trusted by Fortune 500 companies, iMerit specializes in complex projects that require domain expertise and scalability.
Service Attributes
Expert-led annotation for computer vision, NLP, LiDAR, and audio data.
Focus on industries like autonomous mobility, healthcare, agriculture, and geospatial tech.
In-house workforce ensures security, quality, and ethical data sourcing.
Supports advanced use cases like LLM fine-tuning and multimodal AI.
Robust data governance and ISO-certified workflows.
Seamless integration with client tools and model feedback loops.
Committed to inclusive AI development with socially impactful hiring.
Hive
Hive provides an end-to-end AI and data labeling platform, combining powerful pre-trained models with human-in-the-loop services to deliver scalable solutions for content moderation, image annotation, and enterprise AI needs.
Distinguished Features
Offers pre-trained APIs for tasks like content moderation, logo detection, and transcription.
Scalable image, video, and text annotation powered by a managed workforce.
Supports use cases across media, advertising, e-commerce, and security.
Human-in-the-loop workflows ensure accuracy and context-aware labeling.
Real-time model deployment with custom training capabilities.
Enterprise-grade platform with strong data privacy and compliance support.
Integrates AI automation with human quality assurance for reliable outputs.
SuperAnnotate
SuperAnnotate is an end-to-end computer vision platform that combines advanced annotation tools, robust collaboration features, and automation to accelerate AI model development with high-quality labeled data.
Top Features
Built-in work collaboration and quality management tools.
Offers annotation automation to increase efficiency and consistency.
Integrates with popular ML pipelines and tools (e.g., CVAT, Label Studio).
Enables version control and project tracking at scale.
Custom workforce options or bring-your-own annotator flexibility.
Dataloop
Dataloop is a data engine for AI that streamlines the entire data lifecycle—from annotation and automation to deployment—enabling teams to build, manage, and improve computer vision applications at scale.
What sets them ahead?
End-to-end platform for data annotation, curation, QA, and model training
Supports image, video, and point cloud annotation with powerful toolsets
Built-in automation and AI-assisted labeling to boost productivity
Scalable workforce management and task orchestration tools
Cloud-native and API-first infrastructure for seamless ML pipeline integration
Real-time collaboration with version control and issue tracking
Used in sectors like retail, agriculture, robotics, and manufacturing
