Red fescue (Festuca rubra L.) variety recognition using subset division and neural networks

The classification of plant varieties is a key task in plant breeding and variety registration. Red fescue (Festuca rubra L.), a widely cultivated grass species, includes numerous closely related varieties, making automated classification a challenging multi-class problem. This study aimed to develop and evaluate a multilayer perceptron (MLP) neural network combined with a subset-based decision framework for accurate classification of red fescue varieties and recognition of previously unseen varieties. The study analyzed 76 varieties described by seven morphological features. To address the complexity of the multi-class problem, the dataset was divided into multiple subsets and the effectiveness of different partitioning strategies was evaluated. A confidence-based and majority-based decision rule (majority ratio ≥ 0.9 and mean Softmax confidence ≥ 0.8) was introduced to improve the reliability of final predictions and enable open set recognition. The model was evaluated using accuracy, precision, F1 score, and recall. The most optimal solution was to divide the dataset into 15 subsets, with the first subset containing six varieties and the remaining subsets containing five varieties each. This approach provided the best balance between predictive performance and decision consistency, enabling correct classification of known varieties and stable detection of unknown samples. Combining MLP neural networks with strategic subset division and confidence-driven decision rules offers a robust solution to high-dimensional, multi-class classification challenges in plant variety recognition. The model's ability to recognize new varieties is crucial for its practical application, ensuring the algorithm's flexibility. This is particularly useful in agriculture and horticulture, where new varieties are bred over the years.