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Our Agri Drone Flight Operation service offers precision drone flights designed to collect essential data for agricultural analysis, whether for crop monitoring, land surveying, or environmental assessments. These flights utilise RGB, multispectral, hyperspectral, and thermal sensors to capture data. The data is collected with aims to help agribusinesses optimise resource management, detect crop health issues, pest management, and make informed decisions. Whether you need to test drone [...]

We offer a specialised service focused on the acquisition and interpretation of image datasets captured through optical sensors. These datasets provide valuable agronomic indicators such as Leaf Area Index (LAI), biomass, plant count, and more. Key insights that reflect crop health and growth patterns could be generated by analysing these data. These image-based datasets are crucial for training algorithms that predict various agronomic parameters, such as plant growth, height, yield potential [...]

We offer experimentation fields for arable crops, horticulture crops, and greenhouse facilities. These environments can be complemented with agronomic validation services, sample laboratory analysis or crop characterisation if required. All combined serve as testbeds for data acquisition throughout the crops’ growth cycle, providing the infrastructure and expertise to support companies in the testing, validation and improvement of their technological solutions in real agricultural conditions [...]

The AI Governance Framework Evaluation service empowers agricultural businesses to leverage AI responsibly. It addresses the unique challenges and opportunities within the sector, from precision farming and livestock management to supply chain optimisation. This service starts with a use-case assessment that highlights similar AI initiatives and identifies potential ethical gaps. Following that, a tailored AI ethics framework is developed, aligned with organisational values and industry best [...]

The AI Ethics Training programme trains engineers working with AI to detect, analyse, and address ethics issues during the design and development phases. As a result, it provides organisations within the agricultural sector the essential knowledge and practical skills required for responsible AI implementation. This service addresses the critical need for teams to understand and learn how to apply ethical principles in their daily work with AI systems. The training covers key areas, including [...]

The AI Explainability Audit service offers independent assessment of AI explainability in agriculture, focusing on the results of AI systems, not the technical details. We examine the explanations provided by AI systems (e.g., for crop management, livestock care, or resource optimisation) to ensure they are clear, accurate, and relevant to real-world farming practices. The service assesses whether these explanations are easily understood by farmers and other stakeholders, identifying any gaps or [...]

Evaluating or verifying AI solutions is technically challenging and imposes specific requirements on the application, which can vary from setting internal benchmarks to establishing safety conditions. Evaluation and verification are performed using a broad set of tools ranging from straightforward performance evaluation metrics to formal methods for the verification of safety requirements. Formal methods can also be used to evaluate requirement fulfilment and systematically discover scenarios [...]

This service concerns training AI models on behalf of the customer for a specific task and optimisation objective, e.g., improving accuracy on crop classification from image data. The target model is the solution provided by the customer that needs to be enhanced with respect to a set of predetermined features to reach the desired performance level. However, if required, the training can also be applied to additional state-of-the-art models available in the market for benchmarking purposes. If [...]

The service allows training an AI model to be used for computer vision tasks, like object detection, segmentation, or classification tasks. The training is run on the TEF infrastructure, thus providing a powerful platform for developing cutting-edge artificial intelligence solutions in the agrifood sector. By leveraging high-performance computing resources, CPUs, and GPUs within the TEF infrastructure, this service enables innovators to train AI models using datasets based on images or videos [...]

In order to train, test, and validate AI applications, annotated datasets are needed. Wageningen Research - Field Crops can provide FAIR datasets consisting of high-quality raw images, annotations, and metadata on crops, weeds, diseases, and pests. At the moment a dataset on volunteer potato in sugar beet and onion is available. Let us know in which other datasets you are interested.

The service focuses on utilising artificial intelligence (AI) and visual recognition technology to enhance quality assurance, consistency, and efficiency in the agri-food industry. Its AI-driven quality assessment solutions ensure that products meet industry standards, regulatory requirements, and consumer expectations. By identifying deviations from quality standards, the service enables proactive quality control, reducing the risk of product recalls or rejections.

We offer a comprehensive testing service to validate the safety functions of autonomous mobile robots and robotic systems. Our tests use a high-precision laser tracker for accuracy to assess your robot's ability to detect and stop when encountering an obstacle, as defined in ISO 18497:2024 (Protocol included in part IV Annex D.2). These tests, conducted at our Montoldre site in France, will help ensure your robot's compliance with industry safety standards. A representative from your team must [...]