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2D图像/照片。图像采集于2020年与2021年大豆种植季，拍摄设备为佳能EOS 7D Mark II数码单反相机及摩托罗拉Moto Z2 Play智能手机，拍摄地点涵盖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勒斯轮作（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的田间地块。两个种植季共采集到9981张原始图像，涵盖8类病害与缺素类别：(1)健康植株，(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）。拍摄过程中，针对每类病害的叶片，一部分在田间仍附着于植株时，于不同冠层高度进行拍摄；另一部分则从植株取下后，立即平铺在修剪过的草地或白色平面上完成拍摄。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，十分适配分类建模相关研究。

搜集汇总

背景与挑战

背景概述

该数据集是一个专注于大豆叶片病害的图像集合，包含2020和2021年采集的9,981张原始图像，覆盖健康叶片及七种常见病害和钾缺乏症状。图像在田间和控制背景下拍摄，适用于植物病害识别和视觉对象识别研究，特别适合用于开发基于卷积神经网络(CNN)的自动分类器进行深度学习分类建模。