Infoscribe handles a wide range of agricultural data from multiple sources, supporting agritech companies, research organizations, and production stakeholders in leveraging their information. The company relies on its expertise in annotation, structuring, and data preparation to transform heterogeneous datasets into resources usable by analytical models, AI systems, or agronomic management tools.

For visual data, Infoscribe processes satellite images, high-resolution drone imagery, and ground photographs taken by farmers, technicians, or embedded sensors. These data are essential for detecting water stress, monitoring growth, identifying diseases, and segmenting fields. Video data from drones or agricultural robots can also be processed for tasks such as counting, object tracking, or crop recognition.

Infoscribe also handles agricultural IoT sensor data, such as soil moisture, temperature, sunlight, pH, chemical composition, and input rates. These time series feed predictive models for yield estimation, irrigation management, or resource planning.

3D data is also supported, including LiDAR point clouds generated by drones, agricultural machinery, or robots. These datasets are useful for biomass analysis, plant structure modeling, or terrain mapping.

Finally, Infoscribe can process documentary data, such as farm records, intervention histories, soil analysis results, crop surveys, or weather data. This information can be extracted, organized, and standardized to complement visual or sensor datasets.

Thanks to this versatility, Infoscribe effectively supports projects requiring reliable, standardized agricultural data that is ready for analysis or integration into intelligent systems.

Infoscribe’s services target all major agricultural sectors, as its data processing, annotation, and structuring solutions address cross-cutting needs in the modernization and digitalization of agricultural practices.

Infoscribe primarily supports the field crops sector (cereals, oilseeds, legumes), where the analysis of satellite images, drone imagery, and sensor data enables growth monitoring, anomaly detection, and yield optimization.

The company also operates in the viticulture sector, which is particularly sensitive to plant health, climate variations, and fine-grained field management. Aerial imagery and predictive models based on annotated data are key tools for anticipating quality and protecting crops.

The fruits and vegetables sector benefits from Infoscribe’s expertise as well, through data processing that enables automated counting, disease identification, row segmentation, and maturity management.

Vegetable, horticultural, and specialty crop sectors (medicinal plants, aromatic plants, etc.) leverage structured datasets to automate monitoring tasks and improve agronomic accuracy.

Finally, precision agriculture activities, including irrigation optimization, soil analysis, and IoT data integration, are fully supported by Infoscribe’s services.

In summary, Infoscribe’s offerings cover a wide range of agricultural sectors, each benefiting from reliable, annotated, and analysis-ready data to support innovation and performance.

Text annotation plays a crucial role in enhancing the analysis and understanding of agronomic data, as it transforms raw information into actionable knowledge for artificial intelligence systems. In agriculture, textual data comes from multiple sources: field observations written by technicians, farm reports, phytosanitary diagnostics, crop histories, weather descriptions, or data from connected sensors. These contents, often heterogeneous and unstructured, require annotation to be properly interpreted by analytical or predictive models.

Text annotation first enables the identification and structuring of key elements in these documents: crop names, phenological stages, observed symptoms, environmental conditions, soil types, performed interventions, or detected risk levels. By consistently labeling this information, AI can automatically recognize patterns, classify situations, or link different agronomic factors together.

Moreover, text annotation helps improve the accuracy of natural language processing (NLP) models used in agritech. By providing reliably annotated examples, models learn to interpret formulations specific to the agricultural domain, often technical or local. This enables automated report reading, extraction of agronomic indicators, and assisted diagnostics.

Text annotation also facilitates the integration of IoT sensor data. When a sensor records an event (temperature fluctuation, soil moisture, disease detection), annotations help link these readings to human descriptions, enhancing the quality of predictive models.

Finally, this textual structuring allows the creation of agricultural knowledge bases, useful for research, crop management, and decision support. Thanks to text annotation, agricultural stakeholders benefit from more comprehensive, faster, and more relevant analyses.

Infoscribe supports a wide variety of agricultural data formats and standards to ensure maximum compatibility with analysis tools, agritech platforms, and decision-support systems. Outputs can be tailored based on client needs and the type of data processed — images, time series, sensor data, 3D models, or technical documents.

For aerial or drone images, common formats such as JPEG, PNG, TIFF, GeoTIFF, and georeferenced orthomosaics are fully supported. These formats allow seamless integration into agricultural imaging software or field monitoring platforms.

For agricultural IoT sensor data, Infoscribe can deliver structured files such as CSV, JSON, Parquet, or real-time databases, suitable for monitoring systems, agronomic dashboards, and predictive models. Time series can be normalized to facilitate processing by analytical or forecasting algorithms.

For point clouds and 3D data, industry-standard formats such as LAS, LAZ, PLY, or XYZ are supported. These formats enable immediate use in 3D mapping software, terrain modeling, or biomass analysis.

Finally, for textual or documentary data, Infoscribe can deliver structured formats such as XML, JSON, CSV, or exports ready for NLP pipelines.

The goal is to adapt to the client’s tools while ensuring clean, standardized, and immediately usable data in agricultural, AI, or geospatial workflows.