Speaker
Description
The assessment of crop variety distinctness, uniformity, and stability (DUS) is a fundamental component of plant breeding and registration processes. Traditionally, one-dimensional analysis of variance is conducted separately for each attribute. However, before conducting separate analyses, it would be worthwhile to apply multivariate methods to determine whether a given variety differs from others simultaneously in all examined attributes. Multivariate evaluations are crucial for selections as they allow for a comprehensive analysis of multiple traits simultaneously, thereby providing a more holistic assessment of a variety's performance. This study introduces a novel approach to distinctness testing based on machine learning, aiming to improve classification accuracy and efficiency in crop variety evaluation. An Artificial Neural Network (ANN) model was developed to classify plant varieties using phenotypic data collected according to DUS guidelines. The network architecture incorporated advanced techniques such as batch normalization and dropout, which enhanced model robustness and reduced overfitting. Furthermore, a new subset division strategy was proposed, ensuring a balanced representation of varieties and trait combinations during model training and validation. The model can effectively recognize both known and previously unseen varieties using, demonstrating generalizability and practical value for breeders. The study highlight the utility of machine learning in supporting variety distinctness assessments, offering flexible tools for agricultural research and plant breeding.
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