| Summary: | In order to address the challenges related to the classification and recognition of soybean disease and healthy leaf identification, it is essential to have access to high-quality images. A meticulously curated dataset named “SoyNet” has been created to provide a clean and comprehensive dataset for research purposes. The dataset comprises over 9000 high-quality soybean images, encompassing healthy and diseased leaves. These images have been captured from various angles and directly sourced from soybean agriculture fields; The soybean leaves images are organized into two sub-folders: SoyNet Raw Data and SoyNet Pre-processing Data. Within the SoyNet Raw Data folder are separate folders for healthy and diseased images captured using a digital camera. The SoyNet Pre-processing Data folder comprises resized images of 256*256 pixels and the grayscale versions of disease and healthy images, following a similar organizational structure. We captured the images using the Nikon digital camera and the Motorola mobile phone camera, utilizing different angles, lighting conditions, and backgrounds. They were taken in different lighting conditions and backgrounds at soybean cultivation fields to represent the real-world scenario accurately. The proposed dataset is valuable for testing, training, and validating soybean leaf disease classification.
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