Deep convolutional neural network for automatic discrimination between Fragaria × Ananassa flowers and other similar white wild flowers in fields

摘要

BackgroundThe images of different flower species had small inter-class variations across different classes as well as large intra-class variations within a class. Flower classification techniques are mainly based on the features of color, shape and texture, however, the procedure always involves too many heuristics as well as manual labor to tweak parameters, which often leads to datasets with poor qualitative and quantitative measures. The current study proposed a deep architecture of convolutional neural network (CNN) for the purposes of improving the accuracy of identifying the white flowers of Fragaria × ananassa from other three wild flower species of Androsace umbellata (Lour.) Merr., Bidens pilosa L. and Trifolium repens L. in fields.