SoyNet: A high-resolution Indian soybean image dataset for leaf disease classification

In order to address the challenges related to the classifi-cation 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 90 0 0 high-quality soybean images, encompassing healthy and diseased leaves. These images have been captured from various an-gles 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 an-gles, lighting conditions, and backgrounds. They were taken in different lighting conditions and backgrounds at soybean cultivation fields to represent the real-world scenario accu-rately. The proposed dataset is valuable for testing, training, and validating soybean leaf disease classification.& COPY; 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )