Fruit Tree Quantity Detection Dataset

The current agricultural industry faces challenges in fruit tree management and yield monitoring. Traditional manual counting methods are inefficient and prone to errors, failing to meet the needs of modern agriculture. Existing image recognition technologies, although making progress in some fields, still face issues of insufficient accuracy and poor adaptability in fruit tree quantity detection. This dataset aims to solve these technical challenges by providing high-quality image samples, improving the accuracy and efficiency of detection. The dataset contains diversified images from different orchards, captured with high-resolution cameras under various lighting conditions to ensure diversity and representativeness. We have implemented multiple labeling rounds and expert reviews as quality control measures to ensure the accuracy and consistency of data annotation. The data is stored in JPEG format, with a clear structure, making it convenient for subsequent analysis and model training.