Ornamental Flower Pear Recognition Image Dataset

Garden management and smart agriculture are increasingly gaining attention in modern society, with significant technical challenges remaining in the efficient recognition of pear blossoms. Current recognition technologies face low accuracy in complex environments, especially under low contrast and varying lighting conditions, where traditional methods can easily result in omissions and misrecognitions. The Ornamental Flower Pear Recognition Image Dataset aims to address these precision and environmental adaptability issues, meeting the needs of intelligent management and precision agriculture. The dataset's images are captured using high-resolution camera equipment under different lighting conditions to ensure diversity and representativeness. Additionally, multiple rounds of annotation and consistency checks are conducted to enhance the accuracy of the annotated data. The annotation team consists of horticultural experts and experienced annotators, with a team size of over 20 people. Data preprocessing includes image denoising, color balancing, and size normalization, ultimately stored in a structured JPG format for convenient model training and evaluation.