Data from: No More Laborious Stem Counting: AI-powered Computer Vision Enables Identification and Quantification of Solid and Hollow Alfalfa Stems at the Pixel Level

The data collected for the article "No More Laborious Stem Counting: AI-powered Computer Vision Enables Identification and Quantification of Solid and Hollow Alfalfa Stems at the Pixel Level" includes image data, labeled JSON data, and machine and deep learning model data. This data was gathered to develop and validate an AI-powered computer vision system designed to accurately identify and quantify solid and hollow alfalfa (<i>Medicago sativa</i> L.) stems at the pixel level. The images were captured under controlled conditions to ensure consistency and quality. The labeled JSON data provides detailed annotations for each image, which were used to train and evaluate the machine and deep learning models. These models were developed to automate the stem counting process, significantly reducing the manual labor involved and improving accuracy. By using this data and the provided models, researchers can reproduce the experiments and achieve the same results, facilitating further research and application in agricultural studies.

本数据集为论文《告别繁琐茎秆计数：人工智能驱动的计算机视觉技术实现像素级识别与量化实心与空心苜蓿茎秆》所采集，包含图像数据、带标注JSON数据以及机器学习与深度学习模型数据。本数据集的采集旨在开发并验证一套人工智能驱动的计算机视觉系统，该系统可在像素级精准识别并量化实心与空心苜蓿（*Medicago sativa* L.）茎秆。所有图像均在可控环境下采集，以保障数据的一致性与质量。带标注的JSON数据为每张图像提供了详细的标注信息，用于训练与评估前述机器学习及深度学习模型。本系列模型旨在实现茎秆计数流程自动化，可大幅降低所需人工工作量并提升计数准确率。研究人员可通过使用本数据集与配套模型复现实验并获得一致结果，从而推动农业研究领域的后续探索与应用。