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Custom AI-Optimized Testing Protocol Design for Agricultural Solutions

Danish Technological Institute (DTI)

We specialise in designing comprehensive testing protocols for the physical testing of customer solutions in the agricultural sector. Our approach focuses on creating consistent procedures that enhance the precision of AI models developed from the collected data. These protocols are carefully crafted to meet individual customer and project needs, incorporating requirements for both laboratory and field testing scenarios. We prioritise automation in data collection to ensure consistent and repeatable testing procedures, which is crucial for high-quality data collection. This service bridges the gap between physical testing and AI model development, providing a solid foundation for advancing agricultural technologies.

Test design

Test setup

Data Analysis for Smart Irrigation Systems

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

Data analysis to optimise AI model training

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 analysis

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

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 augmentation

Data Collection for Advanced Agricultural Environmental Monitoring and Analysis Systems

Danish Technological Institute (DTI)

We offer cutting-edge technical systems, both hardware and software, along with expert knowledge for collecting and analysing relevant environmental data during agricultural testing. Our service emphasises the use of standardised protocols to ensure enhanced accuracy and efficiency. We provide a comprehensive suite of monitoring and analysis tools, including methane detection systems, PTR-MS for volatile organic compound analysis, GHG measurement using CRDS technology, and automated climate data collection using IoT sensors and AI algorithms. These advanced systems enable precise environmental monitoring and data analysis for agricultural research and development.

AI model training

Collection of test data

Cybersecurity

Data analysis

Data augmentation

Performance evaluation

Provision of datasets

Test design

Test execution

Test setup

Data Collection using UGVs and modular sensor payloads

Fondazione Bruno Kessler (FBK)

This service consists of deploying a modular sensor payload (equipped with Lidar, inertial motion units, GNSS, Radar, stereo cameras, and ultra-wideband technology) to agricultural environments such as vineyards and orchards to collect and annotate high-fidelity datasets tailored to customer needs. Data are systematically labelled and can be delivered to agrifoodTEF customers as structured datasets for AI-driven agricultural solutions.

Collection of test data

Data labelling

Politecnico di Milano (POLIMI)

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 augmentation

Data pipeline evaluation for AI applications

Research Institutes of Sweden (RISE)

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.

AI model training

Collection of test data

Data analysis

Performance evaluation

Test execution

Test setup

Data Processing and Analysis

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 analysis

Desk assessment

Performance evaluation

Provision of datasets

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

Gradiant Technologies

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 analysis

Desk assessment

Data validation and processing

Politecnico di Milano (POLIMI)

Through this service, we offer customers the ability to validate and augment the data collected during the different testing phases. Examples of operations that this service can provide include quality checks (e.g., presence of all datastreams, presence of drop-outs, technical quality such as focus or exposure of camera images, agronomic significance of data); preparation for performance evaluation (e.g., identification of the images containing selected visual markers or specific plants, association between images from the system under test and the ground truth); data processing (e.g., transcoding) and augmentation (e.g., generation and incorporation of metadata). Data labelling (e.g., to enable training of AI models) is not included, as it is the object of a separate service (S00291).

Data analysis

Data augmentation

Data validation and processing services

Lukasiewicz Poznanski Instytut Technologiczny

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.

Collection of test data

Data analysis

Performance evaluation

Catalogue of Services

All agrifoodTEF services can be customised!