Catalogue of Services

Crop Dataset Generation

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek

Location

At user's premises

Belgium

Remote

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

In the Crop Dataset Generation service, we collect large-scale datasets on a frequent basis using drones or autonomous robots. AI and data-driven solutions require extensive data, shifting the focus from limited feature engineering to acquiring large, balanced datasets of images and labels for training convolutional deep-learning networks. We offer flexible data collection options tailored to customer needs. We can use our systems and fields to gather data on crops under various conditions, such as different lighting, angles, growth stages, and soil types. Alternatively, we can use customer-provided devices to collect data for testing and validation, such as evaluating sensor performance in detecting heat in crops or testing robot navigation in crop fields.

Data Analysis for AI Performance Evaluation/Assessment

Location

France

Viticulture

When you’re developing an AI-based solution, you need to assess its accuracy and its reliability. Whether it is for detection or prevision purposes, we can compare the outcomes of AI with ground truth and measure the accuracy of the detection or prevision of an AI-based model. If the model needs improvements, we can provide new datasets; see S00102.

Data Analysis for Smart Irrigation Systems

HISPATEC

Location

Spain

Arable farming

Greenhouse

Horticulture

Tree Crops

Viticulture

This service allows companies to experiment with real-time data and advanced analytics to improve water management, reduce waste, and enhance crop yield. The service includes comprehensive testing and evaluation to ensure compatibility and performance, ensuring efficient irrigation practices and sustainability through insights into soil moisture, weather conditions, and plant water needs.

Data analysis to optimise AI model training

Josephinum Research

Location

Remote

Arable farming

Horticulture

Livestock farming

Tree Crops

Viticulture

Our service enhances your dataset's quality using descriptive statistics to gain insights and ensure robustness. We use measures like mean, median, standard deviation, etc., to identify central tendencies and variability. We perform plausibility filtering to check for logical consistency and outlier filtering to remove data points that could skew predictions. This careful data preparation refines your dataset, boosting the accuracy and reliability of your model training. Tailored to your needs, these methods optimise your dataset for better model performance.

Data augmentation to optimise AI model training and prevent the model from overfitting

Josephinum Research

Location

Austria

Remote

Arable farming

Horticulture

Livestock farming

Tree Crops

Viticulture

In preparing image or sensor data, pre-processing is crucial. We apply techniques like normalisation for data uniformity and correlation analysis to uncover patterns, enhancing interpretability. Dimension reduction optimises computational efficiency, while adding randomised noise and artificial expansion improves model generalisation. We implement anonymisation to protect data privacy and ensure precise data labelling for supervised learning. These methods, tailored to your needs, enhance your data's quality and usability.

Data labelling

Politecnico di Milano

Location

Italy

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service concerns the curation of ground truth annotations for testing and experimentation data. More specifically, data labelling concerns the association of portions of data (e.g., image regions) to descriptions that provide information about the significance of those portions. In particular, labelling is required whenever the data are meant to be used for model training. We also provide labelling services for domain-specific data attributes, such as disease indicators and their confounding factors, by experts in agronomy. The labelled data will be accompanied by a report synthesising quality metrics and statistics like the number of data points annotated, the percentage of the full set covered for the annotations, the number of annotators and the inter-annotator agreement.

Data labelling for AI detection purposes in the vineyard

Institut Français de la Vigne et du Vin - IFV

Location

France

Viticulture

You can provide your own dataset, without labels, or ask us to build the dataset and label it so you can train an AI model. Labelling can be made with your own tools or not, online or offline, it can be segregation or segmentation.

Data pipeline evaluation for AI applications

RISE - Research Institutes of Sweden

Location

Remote

Sweden

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The purpose of the service is to ensure that data protocols and pipelines are adequate to the AI application developed by the customer. The service can be delivered at different stages of product maturity to ensure that it can provide targeted testing when it is most useful. During earlier product development stages, the service concentrates on data collection, quality and storage protocols, whilst, at later stages, attention can be extended to scalability, access and control mechanisms.

Data Processing and Analysis

Instituut voor Landbouw-, Visserij- en Voedingsonderzoek

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

This service transforms your raw agricultural data into valuable insights, enabling clear decision-making and optimised performance. The received data—whether from cameras, crop sensors, or other IoT devices—undergoes a thorough analysis to remove errors, inconsistencies, and outliers. Our pipeline prepares a balanced, high-quality, standardised dataset that complies with data standards, ensuring seamless sharing and interpretation. The dataset is tailored to the specific requirements of your project and delivers clean, organised data, ready for reporting, sharing, or further use in predictive models, trend analysis, or resource optimisation.

Data quality analysis of data to be used in agrifood AI or robotics systems

GRADIANT

Location

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

Given a dataset intended to be used in AI or robotics applications for the agrifood sector, a quality analysis will be performed to search for issues in the provided data. Thus, the customer can ensure their data is reliable and suitable for use in this kind of application. The dataset could be an input or an output of an AI process. For example, it could be data to train a predictive system or the output of that predictive system (which allows detecting prediction problems, like predictions out of range or not following the expected output distribution). The kind of issues to search for range from missing values to problems related to data out of distribution. As a result of this service, the client will receive a report with the conclusions and recommendations on how to improve the data, if needed.

Data validation and processing services

Lukasiewicz - Poznanski Instytut Technologiczny

Location

Poland

Remote

Arable farming

Food processing

Greenhouse

Horticulture

Livestock farming

Tree Crops

Viticulture

The service will provide validation and processing of data sets, in particular video data, coming from AI-based systems testing and/or digital measurement and data collection in cloud technologies. The scope of the proposed data validation will include, among other things, the service provider's activities of checking the presence of all data streams in the analysed image or video files, detecting any breaks in transmission, and checking the focus or exposure of the camera images. The next stage of the service will be the preparation of the material for evaluating the performance of AI-based technical systems under test, e.g., by formatting, identifying images containing selected visual markers, or linking images from a given system under test to data of other modalities.