Applying convolutional neural networks for mustard variety recognition

The aim of this study was to develop and apply a Convolutional Neural Network (CNN) model to recognize and classify white mustard (Sinapis Alba L.) varieties, addressing the complex task of discriminating among 57 varieties. Utilizing a one-dimensional CNN model, the research focused on multivariate analysis based on a set of 15 traits. The CNN architecture included convolutional layers, batch normalization, pooling, flattening, dropout, and dense layers. The model demonstrated effectiveness in classifying varieties, achieving high accuracy and providing valuable insights into potential new varieties. Subset division, a new approach, was applied. Evaluation metrics, including accuracy, F1 score, precision, and recall, were calculated for eight subsets, confirming the model's robust performance. While this study uses mustard as an illustrative example, the method is not limited to this crop and can be extended to other agricultural crops, with potential modifications depending on the specific traits relevant to each crop. The approach contributes to agricultural advancements, offering a reliable tool for breeders to assess variety distinctness and streamline the testing process. The model’s ability to detect unknown varieties further enhances its utility in agricultural research covering a comprehensive and impactful advancement in variety classification.