Pictures of diseased soybean leaves by category captured in field and with controlled backgrounds: Auburn soybean disease image dataset (ASDID)
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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.
本数据集包含染病大豆叶片的二维图像与照片，非常适用于植物病害识别及视觉目标识别相关研究。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数码单反相机以及摩托罗拉Moto Z2 Play智能手机，采集场地分别为阿拉巴马州塔拉斯西的EV Smith农业研究站、阿拉巴马州奥本的Cullars Rotation试验区，以及阿拉巴马州布鲁顿的布鲁顿农业研究单元。两个生长季累计收集原始图像共计9981张，涵盖8类病害/缺素类别：(1)健康植株，以及表现出以下症状的植株：(2)细菌性疫病、(3)尾孢叶斑病、(4)霜霉病、(5)蛙眼叶斑病、(6)大豆锈病、(7)靶斑病、(8)钾素缺素症。针对每一类病害，图像采集方式分为两种：部分叶片仍附着于田间植株上，在不同冠层高度进行拍摄；另有部分叶片被摘下后，立即平铺于修剪后的草地或白色平面上完成拍摄。本数据集的采集初衷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，非常适配分类建模任务。
