Testing of Precision Weed Identification

This service supports the evaluation of a CNN-based weed identification system tailored to the customer's specific dataset of weed species. By analysing the network's performance on training and unseen data, we identify weaknesses either in the algorithm or within the dataset itself. Our insights lead to concrete suggestions for enhancing model accuracy, improving training quality, and achieving more reliable recognition outcomes. 

The service begins with an in-depth analysis of the neural network’s architecture and the dataset used for training, including class distribution, image resolution and quality, and annotation methods. Next, we test the network on data excluded from the training phase—such as unlabelled but available field images—to assess its generalisation capabilities. An overall performance review follows, highlighting critical issues, minor deficiencies, and optimisation opportunities, along with clear recommendations for improvements. 

The service is fully customisable to the customer’s needs, including the use of specific datasets and neural network architectures. Customers can also define preferred performance metrics and evaluation indices for the analysis, allowing us to tailor the feedback and optimisation strategies accordingly.