Multi-Class Fruit Leaf Classification Dataset (10 Classes)

This dataset comprises 3,173 high-quality images of healthy fruit leaves from 10 different classes, specifically curated for research in plant classification, species identification, and agricultural analysis using deep learning and computer vision techniques. The dataset includes images of Aegle marmelos (336), Black plum (304), Custard Apple (304), Guava (325), Jackfruit (311), Lotkon (306), Lychee (312), Mango (330), Plum (302), and Star Fruit (343). Each image was captured under diverse environmental conditions to ensure a robust dataset for training and evaluating machine learning models. Since all images represent healthy leaves, this dataset can serve as a baseline for plant disease detection, enabling researchers to compare healthy and diseased samples effectively. It is well-suited for image classification, feature extraction, transfer learning, and species recognition in the fields of agriculture and botany. Potential applications include training convolutional neural networks (CNNs) and transformer-based models for fruit leaf classification, fine-tuning pre-trained models, and developing AI-driven plant monitoring and smart agriculture solutions. The dataset also serves as a valuable resource for augmenting existing datasets to improve model generalization. Researchers and AI practitioners can leverage this dataset to advance precision agriculture and plant health monitoring. For any inquiries or collaboration, please contact the authors.

本数据集共包含来自10个类别的3173张高质量健康果树叶片图像，专为基于深度学习与计算机视觉技术的植物分类、物种识别及农业分析研究定制打造。本数据集涵盖的类别及对应图像数量如下：木橘（Aegle marmelos，336张）、黑果蒲桃（Black plum，304张）、番荔枝（Custard Apple，304张）、番石榴（Guava，325张）、波罗蜜（Jackfruit，311张）、Lotkon（306张）、荔枝（Lychee，312张）、芒果（Mango，330张）、李（Plum，302张）、杨桃（Star Fruit，343张）。所有图像均在多样化的环境条件下采集，以确保本数据集具备足够鲁棒性，可用于机器学习模型的训练与评估。由于本数据集所有样本均为健康叶片，因此可作为植物病害检测任务的基准数据集，帮助研究人员高效对比健康与染病样本。本数据集非常适用于农业与植物学领域的图像分类、特征提取、迁移学习及物种识别任务。其潜在应用场景包括：训练卷积神经网络（Convolutional Neural Networks, CNNs）与基于Transformer的模型以实现果树叶片分类、微调预训练模型，以及开发AI驱动的植物监测与智慧农业解决方案。本数据集同时可作为扩充现有数据集的宝贵资源，以提升模型的泛化能力。研究人员与AI从业者可借助本数据集推动精准农业与植物健康监测领域的发展。如有任何咨询或合作意向，请联系数据集作者。