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Overview
This service helps businesses and organisations evaluate how well their AI models perform in real-world conditions. We conduct thorough testing of your AI model using either your own dataset or our reference datasets, depending on your needs (see the related services). Our evaluation process goes beyond basic accuracy metrics to provide a comprehensive understanding of your model's strengths and limitations. We analyse various aspects of performance that matter for your specific use case, whether it's detecting diseases in crops, sorting produce, or monitoring livestock behaviour. We work with you to determine the most relevant performance metrics for your application, taking into consideration factors like accuracy, speed, reliability, and resource usage. This helps you understand if your AI solution is ready for deployment, needs improvement, or requires adjustments for specific conditions. The evaluation provides clear, actionable insights about your model's performance, helping you make informed decisions about its readiness for real-world agricultural applications.
More about the service
Discover more about our service, including how it can benefit you, the delivery process, and the options for customisation tailored to your specific needs!
This service addresses a crucial challenge faced by companies developing AI solutions for agriculture: knowing whether their AI models are truly ready for real-world deployment. Before using our service, you may be uncertain about your model's actual performance capabilities and limitations.
You might have questions about how well it handles different scenarios, edge cases, or varying conditions typical in agricultural settings.After using our evaluation service, you'll have a clear, comprehensive understanding of your AI model's performance. You'll know exactly how well it performs across different metrics that matter for your specific use case. For example, if you have an AI model for crop disease detection, you'll learn not just its overall accuracy, but also its false positive rate, how well it performs under different lighting conditions, and its speed of detection. This information helps you make confident decisions about deployment, identify areas needing improvement, and understand any limitations that need to be addressed before putting the model into production.
The service transforms uncertainty about your AI model's capabilities into actionable insights, helping you save time and resources by identifying potential issues before real-world deployment. This knowledge is particularly valuable for meeting regulatory requirements, ensuring reliability in agricultural applications, and building trust with end users.
You might have questions about how well it handles different scenarios, edge cases, or varying conditions typical in agricultural settings.After using our evaluation service, you'll have a clear, comprehensive understanding of your AI model's performance. You'll know exactly how well it performs across different metrics that matter for your specific use case. For example, if you have an AI model for crop disease detection, you'll learn not just its overall accuracy, but also its false positive rate, how well it performs under different lighting conditions, and its speed of detection. This information helps you make confident decisions about deployment, identify areas needing improvement, and understand any limitations that need to be addressed before putting the model into production.
The service transforms uncertainty about your AI model's capabilities into actionable insights, helping you save time and resources by identifying potential issues before real-world deployment. This knowledge is particularly valuable for meeting regulatory requirements, ensuring reliability in agricultural applications, and building trust with end users.
The service execution begins with an initial consultation to understand your AI model and specific evaluation needs. You'll need to provide us with your trained AI model and, if applicable, your dataset. If you're using TEF available datasets, we'll help you select the most appropriate ones for your use case.
Finally, if you need a new dataset tailored to this specific case, we offer some other services to create datasets in different scenarios (see S00243 or S00254 in the related services section).The evaluation process typically takes 2-4 weeks, depending on the complexity of your model and the required scope of testing. The evaluation is performed remotely through our secure testing environment, meaning there's no requirement for your physical presence.Upon completion, you'll receive a comprehensive evaluation report that includes detailed performance metrics, visualisations of the results, and specific insights about your model's behaviour.
The report will highlight both strengths and areas for potential improvement, along with recommendations for optimisation if needed. We also provide a technical appendix with all raw performance data for your reference. To begin the service, you need to provide your trained AI model in a standard format (we support most common frameworks, like, for example, TensorFlow, PyTorch, XGBoost, or scikit-learn, among others), documentation describing your model's intended use and current implementation, and if you're using your own dataset, the properly formatted data along with any relevant annotations or labels.
We'll provide clear technical specifications for how to prepare and transfer these materials securely to our testing environment. Throughout the evaluation process, we will maintain regular communications to ensure the testing aligns with your specific needs and to address any questions that arise. Additional consultation sessions can be scheduled as needed to discuss the results in detail.
Finally, if you need a new dataset tailored to this specific case, we offer some other services to create datasets in different scenarios (see S00243 or S00254 in the related services section).The evaluation process typically takes 2-4 weeks, depending on the complexity of your model and the required scope of testing. The evaluation is performed remotely through our secure testing environment, meaning there's no requirement for your physical presence.Upon completion, you'll receive a comprehensive evaluation report that includes detailed performance metrics, visualisations of the results, and specific insights about your model's behaviour.
The report will highlight both strengths and areas for potential improvement, along with recommendations for optimisation if needed. We also provide a technical appendix with all raw performance data for your reference. To begin the service, you need to provide your trained AI model in a standard format (we support most common frameworks, like, for example, TensorFlow, PyTorch, XGBoost, or scikit-learn, among others), documentation describing your model's intended use and current implementation, and if you're using your own dataset, the properly formatted data along with any relevant annotations or labels.
We'll provide clear technical specifications for how to prepare and transfer these materials securely to our testing environment. Throughout the evaluation process, we will maintain regular communications to ensure the testing aligns with your specific needs and to address any questions that arise. Additional consultation sessions can be scheduled as needed to discuss the results in detail.
The evaluation service can be tailored in several ways to match your specific needs. We can customise the evaluation metrics based on your application—for instance, if response time is critical for your use case, we can emphasise performance speed and latency measurements.
If accuracy in specific conditions is paramount, we can focus on detailed analysis of edge cases and failure modes.
- The testing process can be customised through:
- Selection of reference datasets best matching your deployment environment
- Definition of custom performance thresholds and success criteria - Focus on specific aspects such as robustness, fairness, or resource efficiency.
- Custom evaluation scenarios that simulate your specific use conditions
There are some important specifications and limitations to be aware of: (1) Your AI model must be provided in one of our supported formats (TensorFlow, PyTorch, ONNX, or similar standard frameworks); (2) the model should be already trained and ready for inference. We don't provide training services as part of this evaluation, but we also offer another service for that purpose (see the related services section); (3) if you're providing your own dataset, it must be properly labelled and annotated in a format agreed upon between both parties.While we can evaluate most types of AI models, certain specialised architectures may require additional preparation time or may not be compatible with all our testing protocols.We recommend discussing any specific requirements or constraints early in the process so we can ensure our service can meet your needs and adapt the evaluation approach accordingly.
If accuracy in specific conditions is paramount, we can focus on detailed analysis of edge cases and failure modes.
- The testing process can be customised through:
- Selection of reference datasets best matching your deployment environment
- Definition of custom performance thresholds and success criteria - Focus on specific aspects such as robustness, fairness, or resource efficiency.
- Custom evaluation scenarios that simulate your specific use conditions
There are some important specifications and limitations to be aware of: (1) Your AI model must be provided in one of our supported formats (TensorFlow, PyTorch, ONNX, or similar standard frameworks); (2) the model should be already trained and ready for inference. We don't provide training services as part of this evaluation, but we also offer another service for that purpose (see the related services section); (3) if you're providing your own dataset, it must be properly labelled and annotated in a format agreed upon between both parties.While we can evaluate most types of AI models, certain specialised architectures may require additional preparation time or may not be compatible with all our testing protocols.We recommend discussing any specific requirements or constraints early in the process so we can ensure our service can meet your needs and adapt the evaluation approach accordingly.
Location
Remote
Type of Sector
Arable farming
Food processing
Greenhouse
Horticulture
Livestock farming
Tree Crops
Viticulture
Type of service
Performance evaluation
Accepted type of products
Data
Design / Documentation
Software or AI model
