The rise of artificial intelligence (AI) and in particular modern machine learning (ML) algorithms during the last decade has been met with great interest in the agricultural industry. While undisputedly powerful, their main drawback remains the need for sufficient and diverse training data. The collection of real datasets and their annotation are the main cost drivers of ML developments, and while promising results on synthetically generated training data have been shown, their generation is not without difficulties on their own. In this paper, we present a development model for the iterative, cost-efficient generation of synthetic training data. Its application is demonstrated by developing a low-cost early disease detector for tomato plants (Solanum lycopersicum) using synthetic training data. A neural classifier is trained by exclusively using synthetic images, whose generation process is iteratively refined to obtain optimal performance. In contrast to other approaches that rely on a human assessment of similarity between real and synthetic data, we instead introduce a structured, quantitative approach. Our evaluation shows superior generalization results when compared to using non-task-specific real training data and a higher cost efficiency of development compared to traditional synthetic training data. We believe that our approach will help to reduce the cost of synthetic data generation in future applications.

Global Data Annotation Tools Market size at US$ 102.38 Billion in 2023, set to reach US$ 908.57 Billion by 2032 at a CAGR of 24.4% from 2024 to 2032.

This dataset provides high-quality, synthetic annotations of crop images, each labeled with disease type and severity level, along with optional metadata such as location and annotator details. It is ideal for training and benchmarking computer vision models for early crop disease detection, supporting agricultural research, precision farming, and disease monitoring initiatives.

The global video annotation service market is experiencing robust growth, driven by the escalating demand for high-quality training data in the artificial intelligence (AI) and machine learning (ML) sectors. The market's expansion is fueled by the proliferation of applications across diverse industries, including medical imaging analysis, autonomous vehicle development (transportation), precision agriculture, and retail analytics. The increasing adoption of computer vision technologies and the need for accurate, labeled video data to train these systems are major catalysts. While precise market sizing requires specific data, a reasonable estimation based on industry reports and the provided information (considering a potential CAGR of 20-25% which is common for rapidly growing tech sectors) would place the 2025 market value at approximately $2.5 Billion, projected to reach $7 Billion by 2033. The market is segmented by application (medical, transportation, agriculture, retail, others) and type of annotation service (video classification, video management, video tagging, video analysis, others). The North American market currently holds a significant share, followed by Europe and Asia Pacific. However, developing economies in Asia Pacific are showing rapid growth potential, driven by increasing digitalization and investments in AI. Key restraints to market growth include the high cost of annotation, the requirement for specialized skills and expertise, and concerns regarding data privacy and security. Nevertheless, the increasing availability of sophisticated annotation tools, the emergence of crowdsourcing platforms, and advancements in automation technologies are progressively mitigating these challenges. The future landscape of the video annotation service market is poised for significant expansion, particularly with the growing adoption of AI in various sectors and continuous innovation in video annotation techniques. This will lead to increased competition amongst the numerous providers mentioned: Acclivis, Ai-workspace, GTS, HabileData, iMerit, Keymakr, LXT, Mindy Support, Sama, Shaip, SunTec, TaskUs, Tasq, and Triyock, driving further market evolution and refinement of services.

The AI data labeling services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across diverse sectors. The market, estimated at $10 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching a market value exceeding $40 billion by 2033. This significant expansion is fueled by several key factors. The automotive industry relies heavily on AI-powered systems for autonomous driving, necessitating high-quality data labeling for training these systems. Similarly, the healthcare sector utilizes AI for medical image analysis and diagnostics, further boosting demand. The retail and e-commerce sectors leverage AI for personalized recommendations and fraud detection, while agriculture benefits from AI-powered precision farming. The rise of cloud-based solutions offers scalability and cost-effectiveness, contributing to market growth. However, challenges remain, including the need for high accuracy in labeling, data security concerns, and the high cost associated with skilled human annotators. The market is segmented by application (automotive, healthcare, retail, agriculture, others) and type (cloud-based, on-premises), with cloud-based solutions currently dominating due to their flexibility and accessibility. Key players such as Scale AI, Labelbox, and Appen are shaping the market landscape through continuous innovation and expansion into new geographical areas. The geographical distribution of the market demonstrates a strong presence in North America, driven by a high concentration of AI companies and a mature technological ecosystem. Europe and Asia-Pacific are also experiencing significant growth, with China and India emerging as key markets due to their large populations and burgeoning technological sectors. Competition is intense, with both large established companies and agile startups vying for market share. The future will likely witness increased automation in data labeling processes, utilizing techniques like transfer learning and synthetic data generation to improve efficiency and reduce costs. However, the human element remains crucial, especially in handling complex and nuanced data requiring expert judgment. This balance between automation and human expertise will be a key determinant of future market growth and success for companies in this space.

The global data annotation and labeling service market was valued at $17,530 million in 2025 and is projected to reach $48,460 million by 2033, exhibiting a CAGR of 8.1% during the forecast period (2025-2033). The market growth can be attributed to the increasing demand for annotated data in various industries, such as autonomous vehicles, healthcare, e-commerce, and agriculture. The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies is another key factor driving the market growth. AI and ML algorithms require large amounts of labeled data to train and improve their performance. Data annotation services provide this labeled data by manually annotating and labeling images, text, audio, and video data. This enables AI and ML algorithms to be more accurate and efficient. Furthermore, the growing trend of outsourcing data annotation services to countries with lower labor costs is also contributing to the growth of the market. Executive Summary

Data annotation and labeling services are essential for developing high-quality AI and ML models. The market is highly fragmented, with many small and medium-sized players. The market is expected to grow at a CAGR of 25% over the next five years, reaching a value of $1.5 billion by 2025.

Key Findings

The top five players in the market are Appen, Infosys BPM, iMerit, Alegion, and Prodigy. The market is geographically concentrated, with North America and Europe accounting for the majority of revenue. The market is driven by the growth of AI and ML, as well as the increasing demand for data annotation and labeling services.

The automated data annotation tools market is experiencing robust growth, driven by the escalating demand for high-quality training data in various sectors like IT & Telecom, BFSI, Healthcare, and Retail. The increasing adoption of artificial intelligence (AI) and machine learning (ML) models, which heavily rely on accurately annotated data, is a primary catalyst. Furthermore, the rising complexity of AI algorithms necessitates larger and more precisely labeled datasets, fueling the market's expansion. While challenges such as the high cost of annotation and the need for skilled human annotators exist, the market is overcoming these hurdles through the development of more efficient and cost-effective automation tools. The market segmentation reveals a strong presence across various application areas, with IT & Telecom and BFSI likely leading in terms of adoption due to their substantial investments in AI-driven solutions. Different annotation types, including image/video, text, and audio, cater to a wide range of AI development needs. The competitive landscape is populated by established players like Amazon Web Services and Google LLC, alongside innovative startups, creating a dynamic market characterized by continuous innovation and competition. Geographic expansion is also a prominent factor, with North America and Europe currently holding significant market shares, but emerging economies in Asia-Pacific are poised for substantial growth due to increasing digitalization and AI adoption. Looking ahead, the market is predicted to exhibit sustained growth driven by ongoing technological advancements and the expanding applications of AI across multiple industries. The forecast period (2025-2033) suggests continued market expansion fueled by factors such as advancements in automation techniques, reduced annotation costs through optimized algorithms, and the expanding scope of AI applications in sectors like autonomous vehicles and precision agriculture. The emergence of new annotation methods and the increasing accessibility of tools will further democratize AI development and drive market growth. Companies are strategically investing in research and development to enhance the accuracy, efficiency, and scalability of their annotation tools. The market's competitive nature fosters innovation, leading to the development of more sophisticated and user-friendly tools that meet the diverse needs of different industries and applications. The market's evolution is expected to be shaped by the ongoing interplay between technological advancements, industry demands, and competitive dynamics.

The AI data labeling services market is experiencing robust growth, driven by the increasing adoption of artificial intelligence across various sectors. The market's expansion is fueled by the critical need for high-quality labeled data to train and improve the accuracy of AI algorithms. While precise figures for market size and CAGR are not provided, industry reports suggest a significant market value, potentially exceeding $5 billion by 2025, with a Compound Annual Growth Rate (CAGR) likely in the range of 25-30% from 2025-2033. This rapid growth is attributed to several factors, including the proliferation of AI applications in autonomous vehicles, healthcare diagnostics, e-commerce personalization, and precision agriculture. The increasing availability of cloud-based solutions is also contributing to market expansion, offering scalability and cost-effectiveness for businesses of all sizes. However, challenges remain, such as the high cost of data annotation, the need for skilled labor, and concerns around data privacy and security. The market is segmented by application (automotive, healthcare, retail, agriculture, others) and type (cloud-based, on-premises), with the cloud-based segment expected to dominate due to its flexibility and accessibility. Key players like Scale AI, Labelbox, and Appen are driving innovation and market consolidation through technological advancements and strategic acquisitions. Geographic growth is expected across all regions, with North America and Asia-Pacific anticipated to lead in market share due to high AI adoption rates and significant investments in technological infrastructure. The competitive landscape is dynamic, featuring both established players and emerging startups. Strategic partnerships and mergers and acquisitions are common strategies for market expansion and technological enhancement. Future growth hinges on advancements in automation technologies that reduce the cost and time associated with data labeling. Furthermore, the development of more robust and standardized quality control metrics will be crucial for assuring the accuracy and reliability of labeled datasets, which is crucial for building trust and furthering adoption of AI-powered applications. The focus on addressing ethical considerations around data bias and privacy will also play a critical role in shaping the market's future trajectory. Continued innovation in both the technology and business models within the AI data labeling services sector will be vital for sustaining the high growth projected for the coming decade.

This data article contains annotation data characterizing Multi Criteria Assessment Methods proposed in the scientific literature by INRA researchers belonging to the Social Science, Agriculture and Food, Rural Development and Environment department. Those researchs aim to on the one hand, to understand the functioning and social and economic developments of agriculture, food processing industries, agribusinesses, food with close links to local and global environmental stakes, and on the other hand, to shed light on public debates and public and private decisions.

Applications of convolutional neural network (CNN)-based object detectors in agriculture have been a popular research topic in recent years. However, complicated agricultural environments bring many difficulties for ground truth annotation as well as potential uncertainties for image data quality. Using YOLOv4 as a representation of state-of-the-art object detectors, this study quantified YOLOv4’s sensitivity against artificial image distortions including white noise, motion blur, hue shift, saturation change, and intensity change, and examined the importance of various training dataset attributes based on model classification accuracies, including dataset size, label quality, negative sample presence, image sequence, and image distortion levels. The YOLOv4 model trained and validated on the original datasets failed at 31.91% white noise, 22.05-pixel motion blur, 77.38° hue clockwise shift, 64.81° hue counterclockwise shift, 89.98% saturation decrease, 895.35% saturation increase, 79.80% intensity decrease, and 162.71% intensity increase with 30% mean average precisions (mAPs) for four apple flower bud growth stages. The performance of YOLOv4 decreased with both declining training dataset size and training image label quality. Negative samples and training image sequence did not make a substantial difference in model performance. Incorporating distorted images during training improved the classification accuracies of YOLOv4 models on noisy test datasets by 13 to 390%. In the context of apple flower bud growth-stage classification, except for motion blur, YOLOv4 is sufficiently robust for potential image distortions by white noise, hue shift, saturation change, and intensity change in real life. Training image label quality and training instance number are more important factors than training dataset size. Exposing models to test-image-alike training images is crucial for optimal model classification accuracies. The study enhances understanding of implementing object detectors in agricultural research.

This data article contains annotation data characterizing Multi Criteria Assessment Methods proposed in the scientific literature by INRA researchers belonging to the Social Science, Agriculture and Food, Rural Development and Environment department. Those researchs aim to on the one hand, to understand the functioning and social and economic developments of agriculture, food processing industries, agribusinesses, food with close links to local and global environmental stakes, and on the other hand, to shed light on public debates and public and private decisions.

This data article contains annotation data characterizing Multi Criteria Assessment Methods proposed in the scientific literature by INRA researchers belonging to the Science for Action and Development department. It develops as primary mission of producing generic and finalised information, and developing methods, tools and knowhow in its fields of competence which are mathematics and informatics applied to the sectors of food, agriculture and the environment.

This data article contains annotation data characterizing Multi Criteria Assessment Methods proposed in the scientific literature by INRA researchers belonging to the Science for Action and Development department. It develops as primary mission of producing generic and finalised information, and developing methods, tools and knowhow in its fields of competence which are mathematics and informatics applied to the sectors of food, agriculture and the environment.

This dataset contains high-resolution synthetic drone imagery of agricultural fields, each image enriched with detailed metadata, geolocation, crop type, and expert-annotated features such as weeds, diseases, and crop health indicators. It supports advanced analytics in precision agriculture, enabling crop health monitoring, automated feature detection, and field management optimization. The dataset is ideal for developing and benchmarking computer vision models and agronomic decision support tools.

Ubaid, M.T.; Javaid, S. Precision Agriculture: Computer Vision-Enabled Sugarcane Plant Counting in the Tillering Phase. Journal of Imaging 2024, 10, 102. https://doi.org/10.3390/jimaging10050102

Description

Plant annotation is the process of identifying and naming certain aspects or characteristics of plant species, usually for research, categorization, or agriculture. This technique is frequently done out manually by specialists or using automated systems that employ picture recognition technologies. Annotations give useful information on plants' morphology, phenology, diseases, and genetic characteristics. They may include labels for anatomical structures. Annotations may also include categorizing plants based on their development stage, health status, or species identification. Plant annotations are used in agriculture to monitor crop development, detect pests and diseases, optimize cultivation practices, and improve production estimates. Additionally, annotated plant datasets are useful resources for training machine learning models for automated plant recognition and analysis tasks.

The images were labeled using the labeling tool "labelImg". The cane under the leaves was labeled. Annotating the images was difficult because the cane section was so little. Labeling needs care and accuracy while drawing a bounded box around the cane. For 175 photos of data, around 18650 bounding boxes were drawn. The bounding boxes were allocated the class name "sugarcane".

This data article contains annotation data characterizing Multi Criteria Assessment Methods proposed in the scientific literature by INRA researchers belonging to the Plant Health and Environment department. Its research aims to contribute to the development of a productive but environmentally safer agriculture by producing both academic and operational knowledge, by providing methods and tools for crop protection, risk and impact assessment, and by contributing to professional and public education.

AgBase Version 2.0 is a curated, open-source, Web-accessible resource for functional analysis of agricultural plant and animal gene products including gene ontology annotations. Its long-term goal is to serve the needs of the agricultural research communities by facilitating post-genome biology for agriculture researchers and for those researchers primarily using agricultural species as biomedical models. AgBase uses controlled vocabularies developed by the Gene Ontology (GO) Consortium to describe molecular function, biological process, and cellular component for genes and gene products in agricultural species. For more information about the AgBase database visit the Educational Resources page or refer to the AgBase publications. AgBase will also accept annotations from any interested party in the research communities. AgBase develops freely available tools for functional analysis, including tools for using GO. AgBase provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website provides Text, BLAST, Taxonomy, and Gene Ontology search functions, and dedicated pages for Animals (channel catfish, cat, chick, bovine, daphnia, dog, horse, pig, salmon, sheep, trout, turkey), Plants (cotton, maize, Miscanthus, pine, poplar, rice, soybean), Microbes (26 taxa), and Parasites (10 taxa). AgBase currently provides 2,069,320 Gene Ontology (GO) annotations to 394,599 gene products in 534 different taxons, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and AgBase provides comprehensive, species-specific GO annotation files for a variety of animal and plant organisms. AgBase hosts several associated databases and provide genome browsers for agricultural pathogens. Comprehensive training resources (including worked examples and tutorials) are available via links to Educational Resources at the AgBase website. Resources in this dataset:Resource Title: Website Pointer to AgBase [Version 2.0]. File Name: Web Page, url: https://agbase.arizona.edu/index.html Provides agricultural plant and animal Gene Ontology (GO) annotation search options, related tools, and genome browsers.

The MegaWeeds dataset consists of seven existing datasets:

- WeedCrop dataset; Sudars, K., Jasko, J., Namatevs, I., Ozola, L., & Badaukis, N. (2020). Dataset of annotated food crops and weed images for robotic computer vision control. Data in Brief, 31, 105833. https://doi.org/https://doi.org/10.1016/j.dib.2020.105833

- Chicory dataset; Gallo, I., Rehman, A. U., Dehkord, R. H., Landro, N., La Grassa, R., & Boschetti, M. (2022). Weed detection by UAV 416a Image Dataset. https://universe.roboflow.com/chicory-crop-weeds-5m7vo/weed-detection-by-uav-416a/dataset/1

- Sesame dataset; Utsav, P., Raviraj, P., & Rayja, M. (2020). crop and weed detection data with bounding boxes. https://www.kaggle.com/datasets/ravirajsinh45/crop-and-weed-detection-data-with-bounding-boxes

- Sugar beet dataset; Wangyongkun. (2020). sugarbeetsAndweeds. https://www.kaggle.com/datasets/wangyongkun/sugarbeetsandweeds

- Weed-Detection-v2; Tandon, K. (2021, June). Weed_Detection_v2. https://www.kaggle.com/datasets/kushagratandon12/weed-detection-v2

- Maize dataset; Correa, J. M. L., D. Andújar, M. Todeschini, J. Karouta, JM Begochea, & Ribeiro A. (2021). WeedMaize. Zenodo. https://doi.org/10.5281/ZENODO.5106795

- CottonWeedDet12 dataset; Dang, F., Chen, D., Lu, Y., & Li, Z. (2023). YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems. Computers and Electronics in Agriculture, 205, 107655. https://doi.org/https://doi.org/10.1016/j.compag.2023.107655

All the datasets contain open-field images from crops and weeds with annotations. The annotation files were converted to text files so it can be used in the YOLO model. All the datasets were combined into one big dataset with in total 19,317 images. The dataset is split into a training and validation set.

Image Annotation Service Market Outlook

The global Image Annotation Service market size was valued at approximately USD 1.2 billion in 2023 and is expected to reach around USD 4.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 15.6% during the forecast period. The driving factors behind this growth include the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industries, which necessitate large volumes of annotated data for accurate model training.

One of the primary growth factors for the Image Annotation Service market is the accelerating development and deployment of AI and ML applications. These technologies depend heavily on high-quality annotated data to improve the accuracy of their predictive models. As businesses across sectors such as autonomous vehicles, healthcare, and retail increasingly integrate AI-driven solutions, the demand for precise image annotation services is anticipated to surge. For instance, autonomous vehicles rely extensively on annotated images to identify objects, pedestrians, and road conditions, thereby ensuring safety and operational efficiency.

Another significant growth factor is the escalating use of image annotation services in healthcare. Medical imaging, which includes X-rays, MRIs, and CT scans, requires precise annotation to assist in the diagnosis and treatment of various conditions. The integration of AI in medical imaging allows for faster and more accurate analysis, leading to improved patient outcomes. This has led to a burgeoning demand for image annotation services within the healthcare sector, propelling market growth further.

The rise of e-commerce and retail sectors is yet another critical growth driver. With the growing trend of online shopping, retailers are increasingly leveraging AI to enhance customer experience through personalized recommendations and visual search capabilities. Annotated images play a pivotal role in training AI models to recognize products, thereby optimizing inventory management and improving customer satisfaction. Consequently, the retail sector's investment in image annotation services is expected to rise significantly.

Geographically, North America is anticipated to dominate the Image Annotation Service market owing to its well-established technology infrastructure and the presence of leading AI and ML companies. Additionally, the region's strong focus on research and development, coupled with substantial investments in AI technologies by both government and private sectors, is expected to bolster market growth. Europe and Asia Pacific are also expected to experience significant growth, driven by increasing AI adoption and the expansion of tech startups focused on AI solutions.

Annotation Type Analysis

The image annotation service market is segmented into several annotation types, including Bounding Box, Polygon, Semantic Segmentation, Keypoint, and Others. Each annotation type serves distinct purposes and is applied based on the specific requirements of the AI and ML models being developed. Bounding Box annotation, for example, is widely used in object detection applications. By drawing rectangles around objects of interest in an image, this method allows AI models to learn how to identify and locate various items within a scene. Bounding Box annotation is integral in applications like autonomous vehicles and retail, where object identification and localization are crucial.

Polygon annotation provides a more granular approach compared to Bounding Box. It involves outlining objects with polygons, which offers precise annotation, especially for irregularly shaped objects. This type is particularly useful in applications where accurate boundary detection is essential, such as in medical imaging and agricultural monitoring. For instance, in agriculture, polygon annotation aids in identifying and quantifying crop health by precisely mapping the shape of plants and leaves.

Semantic Segmentation is another critical annotation type. Unlike the Bounding Box and Polygon methods, Semantic Segmentation involves labeling each pixel in an image with a class, providing a detailed understanding of the entire scene. This type of annotation is highly valuable in applications requiring comprehensive scene analysis, such as autonomous driving and medical diagnostics. Through semantic segmentation, AI models can distinguish between different objects and understand their spatial relationships, which is vital for safe navigation in autonomous vehicles and accurate disease detectio