Pictures of diseased soybean leaves by category captured in field and with controlled backgrounds: Auburn soybean disease image dataset (ASDID)

The dataset contains 2D images/photographs of diseased soybean leaves ideal for plant disease identification and visual object recognition research. Images were captured during the 2020 and 2021 soybean seasons using a Canon EOS 7D Mark II Digital SLR Camera and a Motorola Moto Z2 Play Smartphone from fields at the EV Smith Agricultural Research Station (Tallassee, Alabama), the Cullars Rotation (Auburn, Alabama), and the Brewton Agricultural Research Unit (Brewton, Alabama). Across both seasons there are a total of 9,981 original images collected across eight disease/deficiency categories. These include (1) healthy-looking plants, and those displaying the symptoms of (2) bacterial blight, (3) cercospora leaf blight, (4) downey mildew, (5) frogeye leaf spot, (6) soybean rust, (7) target spot, and (8) potassium deficiency. For each disease category, leaves were photographed at various canopy heights while still attached to the plant in the field or they were detached from the plant and then immediately photographed while laid flat on the ground in trimmed grass or on a white surface. Images were collected with the goal of developing a Convolutional Neural Network (CNN)-based automated classifier of digital images of soybean diseases. Dataset is well-suited for classification modeling.本数据集包含适用于植物病害识别与视觉目标识别研究的染病大豆叶片二维图像/照片。图像采集于2020年与2021年大豆生长季，拍摄设备包括佳能EOS 7D Mark II数码单反相机（Canon EOS 7D Mark II Digital SLR Camera）以及摩托罗拉Moto Z2 Play智能手机（Motorola Moto Z2 Play Smartphone），采集地点分别为美国阿拉巴马州塔拉斯西的EV史密斯农业研究站、阿拉巴马州奥本的卡拉斯轮作试验田（Cullars Rotation）以及阿拉巴马州布鲁顿的布鲁顿农业研究单元（Brewton Agricultural Research Unit）。两个生长季累计采集原始图像共9981张，涵盖8类病害/缺素类别。具体包括：(1) 健康植株，以及表现出以下症状的染病植株：(2) 细菌性疫病（bacterial blight）、(3) 尾孢叶枯病（cercospora leaf blight）、(4) 霜霉病（downey mildew）、(5) 蛙眼叶斑病（frogeye leaf spot）、(6) 大豆锈病（soybean rust）、(7) 靶斑病（target spot）、(8) 钾素缺素症（potassium deficiency）。针对每类病害类别，采集图像时部分叶片仍附着于田间植株上，并在不同冠层高度进行拍摄；另有部分叶片被采摘后，立即平铺于修剪过的草地或白色平面上进行拍摄。本数据集的采集目标为开发基于卷积神经网络（Convolutional Neural Network, CNN）的大豆病害数字图像自动分类器，非常适用于分类建模任务。

搜集汇总

背景与挑战

背景概述

该数据集是一个包含9,981张原始图像的大豆叶片疾病图像集合，覆盖八个类别，包括健康植物和七种常见疾病症状，图像采集于2020年和2021年的大豆季节，使用专业相机和智能手机在田间和受控背景下拍摄。数据集专为植物疾病识别和视觉对象识别研究设计，特别适用于基于卷积神经网络（CNN）的分类建模，提供多样化的背景条件以增强模型泛化能力。