Overview
Our digital test helps you evaluate how well your model identifies different types of plants. Your model can be delivered in a docker container or the inference results are collected in real time from your device in the field. We focus on key performance metrics like accuracy, precision, recall, Intersection over Union (IoU), and F1 score to give you a clear picture of your model's effectiveness. Crucial to this process is the ground truth data, which can be either specially prepared for this test or sourced from an existing benchmark dataset.
Interested in this Service? Contact us: agrifoodtef[at]josephinium.at
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 testing gives you clear, objective insights into your plant detection system’s performance and helps identify any potential issues.
The test can be done remotely via a prepared docker container. The test itself will take up to a month to execute, depending on the images delivered, the plants in the images and the growth state of those plants. After the test, you will receive a report on detection quality in terms of metrics like accuracy, precision, recall, Intersection over Union (IoU), and F1 score. Seasonally, the test is possible the whole year, preferred in the winter months.
The test can be customised at various levels. One of the main parameters are the type of plant to be recognized by your model. Another variable parameter is the vision task you are solving with the model, e.g. Object Detection, Segmentation, etc..
Location
Remote
Type of Sector
Arable farming
Horticulture
Tree Crops
Accepted type of products
Software or AI model
