Triangular Maple Recognition Image Dataset

In modern agriculture and forestry, accurate identification of plant species is an important link in improving production efficiency and protecting biodiversity. Currently, plant identification often relies on manual experience, which is not only time-consuming and labor-intensive but also prone to errors. Although existing image recognition solutions have made some improvements, there is still significant room for improvement in recognition accuracy and speed. This dataset is dedicated to solving the problem of accurate recognition of the triangular maple in image recognition, meeting the actual needs of garden management and plant monitoring. Data collection is carried out through high-resolution camera equipment under various lighting conditions to ensure data diversity and realism. The data has undergone multiple rounds of annotation and consistency checks by professional horticulturists to ensure high quality. The annotation team consists of 20 experts with extensive experience in agriculture and biology. Data preprocessing includes operations such as exposure and contrast adjustment, and image enhancement, and is ultimately stored in JPG format and organized by category. The images provided in this dataset are of high resolution with multi-angle views, with annotation accuracy exceeding 95%. By introducing image enhancement technology and new plant annotation methods, recognition performance is significantly improved, which can be used to develop more intelligent plant recognition systems. Compared with similar datasets, it has a richer variety of flower species and environmental diversity, providing a more challenging test environment for plant recognition algorithms, while possessing high scalability, making it suitable for different kinds of plant recognition tasks.