Garden Flower Snapdragon Identification Image Dataset

Currently, the garden and agricultural industries face challenges in the identification and management of plant species. Traditional methods rely on expert experience, which is difficult to apply on a large scale and can lead to misidentification in the case of a wide variety of plant species. Some of the existing automated solutions rely on limited samples and cannot accurately identify different growth stages of various plants. Our Garden Flower Snapdragon Identification Image Dataset aims to promote the development of automated recognition technology with high-quality annotated data. This dataset mainly addresses the fine-grained classification problem in plant image recognition, meeting the needs of precise management and care.Data collection is conducted entirely under natural light, using professional digital cameras in diverse natural environments to ensure the reality and diversity of samples. After collection, the data underwent three rounds of annotation and was reviewed for accuracy through consistency checks and horticultural experts. A team of ten with backgrounds in botany and computer vision is responsible for the specific annotation work. Data preprocessing includes steps such as image denoising, cropping, and enhancement to ensure efficient model training. Finally, the data is stored in JPG format and organized hierarchically by flower category.This dataset performs excellently on several data quality metrics; for example, it has an annotation accuracy of over 95% and 98% in consistency testing. The innovative annotation method incorporates deep learning-assisted tools, improving annotation speed and accuracy, enhancing dataset production performance by 25%. By introducing image enhancement techniques, models trained on the dataset improved plant identification accuracy by 15% compared to traditional methods. Compared to other similar datasets, our dataset has significant advantages in terms of category diversity and photo quality, and its scalability makes it applicable in widely related fields for plant recognition and classification tasks.