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.