Mulberry Leaf Dataset

Data collection:
We collected the mulberry leaf cultivars from three regions of Thailand (northern, central, and northeast) that included five provinces in total (Chiang Mai, Phitsanulok, Nakhon Ratchasima, Burriram, and Mahasarakham). DSLR and phone cameras were used to take images with different perspectives from ten mulberry leaf cultivars recorded in the natural environments with different perspectives. The mulberry leaf dataset includes 5,262 images of 10 mulberry leaf cultivars: King Red, King White, Taiwan Maechor, Taiwan Strawberry, Black Austurkey, Black Australia, Chiang Mai 60, Buriram 60, Kamphaeng Saen 42, and Mixed Chiang Mai 60+Buriram 60.

Data description:
The mulberry leaf dataset was curated in 2020 using digital single-lens reflex (DSLR) and smartphone cameras to collect 5,262 images categorized into ten classes existing in diverse environmental conditions. No seasonal considerations were factored in during the data collection; however, all data was collected on the sunny days. There is no existence of external plants in the samples of the mulberry leaves, but there are some natural backgrounds of the brown soil and the mulberry tree, which exist in small proportion relative to the mulberry leaves. The image format of the mulberry leaf dataset exists in JPEG format and has varying resolution sizes. The researcher captured mulberry leaf images from various regions and provinces in Thailand for eight months. The image captures of mulberry leaves were taken from five Thai areas, as shown in Fig. 1. Further, the dataset was annotated by a domain expert responsible for classifying each of the mulberry leaves into their respective classes or categories. The leaves with similar features or properties were stored in a specific folder (class), resulting in ten possible classes.

Data source location:
The mulberry leaf dataset was collected from three regions of Thailand: northern (Chiang Mai), central (Phitsanulok), and northest (Nakhon Ratchasima, Burriram, and Mahasarakham).

Related research article:
Chompookham, T. & Surinta, O. (2021). Ensemble methods with deep convolutional neural networks for plant leaf recognition. ICIC Express Letters, 15(6), 553-565. DOI: 10.24507/icicel.15.06.553

数据采集：
我们从泰国北部、中部与东北部三个区域的共计5个省份（清迈、彭世洛、呵叻、武里南、玛哈沙拉堪）采集桑树品种样本。使用数码单反相机（Digital Single-Lens Reflex, DSLR）与手机摄像头，在自然环境下针对10个桑树品种从不同视角拍摄图像。本桑树叶片数据集包含10个品种的共5262张图像，具体品种为：红国王（King Red）、白国王（King White）、台湾梅乔（Taiwan Maechor）、台湾草莓（Taiwan Strawberry）、黑奥斯特基（Black Austurkey）、黑澳大利亚（Black Australia）、清迈60号、武里南60号、甘烹沙恩42号（Kamphaeng Saen 42）以及清迈60号与武里南60号的混合品种。

数据说明：
本桑树叶片数据集于2020年构建，采用数码单反相机与智能手机采集图像，共收录5262张样本，划分为10个类别，拍摄场景涵盖多样化环境条件。数据采集过程未考虑季节因素，但所有图像均采集于晴天。桑叶样本中无其他外来植物，仅存在少量占比低于桑叶本身的自然背景，包括棕褐色土壤与桑树本体。数据集图像格式均为JPEG，分辨率尺寸存在差异。研究人员耗时8个月，从泰国各区域与省份采集桑叶图像，拍摄地点覆盖泰国5个区域，如图1所示。此外，数据集由领域专家完成标注，负责将每张桑叶划分至对应类别。特征相似的叶片被归入同一文件夹（类别），最终形成10个类别。

数据来源地：
本桑树叶片数据集采集自泰国三大区域：北部（清迈）、中部（彭世洛）以及东北部（呵叻、武里南与玛哈沙拉堪）。

相关研究论文：
Chompookham, T. 与 Surinta, O.（2021）。《结合深度卷积神经网络的集成方法用于植物叶片识别》，ICIC Express Letters，15(6)，553-565。DOI: 10.24507/icicel.15.06.553

搜集汇总

背景与挑战

背景概述

Mulberry Leaf Dataset是一个包含5,262张桑叶图像的数据集，涵盖10个品种，采集自泰国不同地区的自然环境。数据集适用于图像分类和深度学习研究，由Mahasarakham大学支持并开放使用。