Evaluation of AI Camera Perception Systems

This service can significantly enhance the development, testing, and evaluation of AI-based monocular RGB camera perception systems by creating tailor-made datasets and annotated pictures, submitting them to your systems (hardware or software link), and analysing responses to create a detailed evaluation report. It helps developers and agricultural professionals create robust, accurate, and reliable AI systems by statistically characterising their behaviour. Each dataset image is based on the original context of each primal picture. The generated pictures will be customised following specifications on variations in vegetation (type, height, maturity, etc.), as well as on the meteorological conditions and obstacle characteristics, such as human posture, body type, and body shape. The variation in parameters concerning the vegetation, the meteorological conditions, and the attributes of obstacles and humans allows for covering a large number of scenarios that create relevant datasets. These are important for training and validating AI algorithms, improving their accuracy and reliability in various agricultural contexts. With this kind of characterisation, developers can analyse the behaviour of AI systems under different conditions, ensuring robust performance and adaptability. This approach ensures that monocular RGB perception AI systems are tested with a dataset that simulates various agricultural operational scenarios.