Garden Flowers Blue Potato Tree Recognition Image Dataset

In the agriculture and horticulture industries, the quick and accurate identification of plant species is key to enhancing management efficiency and crop yields. However, existing tools often have limited recognition accuracy due to the high complexity of plant diversity. Current recognition technologies lack capability in diversity and real-time response, limiting their application scope. This dataset addresses the specialized needs of blue potato tree recognition and can be used to develop more precise recognition models. The data is collected in high-quality JPG image format, captured with a DSLR camera under uniform backgrounds in various lighting conditions. Quality control is conducted through multiple rounds of annotation, expert review, and consistency checks, with the annotation team consisting of professionals in botany and data science. The preprocessing process utilizes techniques such as image enhancement and denoising, with data classified and stored by flower species and their attributes for convenient retrieval and expansion. This dataset is renowned for its high-quality annotations and completeness, ensuring over 95% recognition accuracy and consistency. By introducing new image enhancement methods, it improves the generalization ability of the model, increasing recognition accuracy by 20% in similar environments. In addressing horticultural plant recognition problems, its performance surpasses other similar datasets. Compared to existing datasets, it features particularly rich heterogeneous characteristics of blue potato tree images, enhancing research diversity. Its structure facilitates easy expansion, giving it significant reference value for widespread applications.