Han Ru, Zheng Ye, Tian Renjie, Shu Lei, Jing Xiaoyuan, Yang Fan
School of Computer Science, Guangdong University of Petrochemical Technology, Maoming, China.
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.
Front Plant Sci. 2025 Jan 15;15:1473558. doi: 10.3389/fpls.2024.1473558. eCollection 2024.
Tea is an important economic product in China, and tea picking is a key agricultural activity. As the practice of tea picking in China gradually shifts towards intelligent and mechanized methods, artificial intelligence recognition technology has become a crucial tool, showing great potential in recognizing large-scale tea picking operations and various picking behaviors. Constructing a comprehensive database is essential for these advancements. The newly developed Tea Garden Harvest Dataset offers several advantages that have a positive impact on tea garden management: 1) Enhanced image diversity: through advanced data augmentation techniques such as rotation, cropping, enhancement, and flipping, our dataset provides a rich variety of images. This diversity improves the model's ability to accurately recognize tea picking behaviors under different environments and conditions. 2) Precise annotations: every image in our dataset is meticulously annotated with boundary box coordinates, object categories, and sizes. This detailed annotation helps to better understand the target features, enhancing the model's learning process and overall performance. 3) Multi-Scale training capability: our dataset supports multi-scale training, allowing the model to adapt to targets of different sizes. This capability ensures versatility and accuracy in real-world applications, where objects may appear at varying distances and scales. This tea garden picking dataset not only fills the existing gap in the data related to tea picking in China but also makes a significant contribution to advancing intelligent tea picking practices. By leveraging its unique advantages, this dataset becomes a powerful resource for tea garden management, promoting increased efficiency, accuracy, and productivity in tea production.
茶叶是中国重要的经济作物，采茶是一项关键的农业活动。随着中国采茶实践逐渐向智能化和机械化方式转变，人工智能识别技术已成为一项关键工具，在识别大规模采茶作业和各种采摘行为方面展现出巨大潜力。构建一个全面的数据库对于这些进展至关重要。新开发的茶园收获数据集具有多项优势，对茶园管理产生积极影响：1）增强图像多样性：通过旋转、裁剪、增强和翻转等先进的数据增强技术，我们的数据集提供了丰富多样的图像。这种多样性提高了模型在不同环境和条件下准确识别采茶行为的能力。2）精确标注：我们数据集中的每一幅图像都用边界框坐标、物体类别和尺寸进行了精心标注。这种详细的标注有助于更好地理解目标特征，增强模型的学习过程和整体性能。3）多尺度训练能力：我们的数据集支持多尺度训练，使模型能够适应不同大小的目标。这种能力确保了在实际应用中的通用性和准确性，因为在实际应用中物体可能以不同的距离和尺度出现。这个茶园采摘数据集不仅填补了中国现有采茶相关数据的空白，还为推动智能采茶实践做出了重大贡献。通过利用其独特优势，该数据集成为茶园管理的强大资源，提高了茶叶生产的效率、准确性和生产力。
