Jadhav Rohini, Suryawanshi Yogesh, Bedmutha Yashashree, Patil Kailas, Chumchu Prawit
Bharati Vidyapeeth College of Engineering, Pune, India.
Vishwakarma University, Pune, India.
Data Brief. 2023 Oct 24;51:109717. doi: 10.1016/j.dib.2023.109717. eCollection 2023 Dec.
We present a comprehensive dataset of 5,323 images of mint (pudina) leaves in various conditions, including dried, fresh, and spoiled. The dataset is designed to facilitate research in the domain of condition analysis and machine learning applications for leaf quality assessment. Each category of the dataset contains a diverse range of images captured under controlled conditions, ensuring variations in lighting, background, and leaf orientation. The dataset also includes manual annotations for each image, which categorize them into the respective conditions. This dataset has the potential to be used to train and evaluate machine learning algorithms and computer vision models for accurate discernment of the condition of mint leaves. This could enable rapid quality assessment and decision-making in various industries, such as agriculture, food preservation, and pharmaceuticals. We invite researchers to explore innovative approaches to advance the field of leaf quality assessment and contribute to the development of reliable automated systems using our dataset and its associated annotations.
我们展示了一个包含5323张薄荷（印度薄荷）叶在各种状态下的图像的综合数据集，这些状态包括干燥、新鲜和变质。该数据集旨在促进叶片质量评估的状态分析领域和机器学习应用方面的研究。数据集中的每个类别都包含在受控条件下拍摄的各种图像，确保了光照、背景和叶片方向的变化。该数据集还包括每张图像的人工注释，将它们分类到各自的状态中。这个数据集有潜力用于训练和评估机器学习算法以及计算机视觉模型，以准确辨别薄荷叶的状态。这可以在农业、食品保存和制药等各个行业实现快速的质量评估和决策。我们邀请研究人员探索创新方法，以推动叶片质量评估领域的发展，并利用我们的数据集及其相关注释为可靠的自动化系统的开发做出贡献。
