SoyNet: Indian Soybean Image dataset with quality images captured from the agriculture field ( healthy and disease Images)
Description
High-quality images of soybean leaf are required to solve soybean disease and healthy leaves classification and recognition problems. To build the machine learning models, deep learning models with neat and clean dataset is the elementary requirement in research. With this objective, this data set is created, which consists of healthy and disease-quality images of soybean named “SoyNet”. This dataset consists of 9000+ high-quality images of soybeans (healthy and Disease quality) with different angles and Images captured direct from the soybean agriculture field to analyze the real problem in research. The images are divided into 2 sub-folders 1) Raw SoyNet Data and 2) Pre-processing SoyNet Data. Each Sub folder contains a digital camera Click, which contains healthy and disease image folders, and 2) Mobile Phone Click, which contains disease images. The Pre-processing SoyNet Data contains folders of 256*256 resized images and grayscale images in a similar manner to disease and healthy data. A Digital-Camera and a Mobile phone with a high-end resolution camera were used to capture the images. The images were taken at the soybean cultivation field in different lighting conditions and backgrounds. The proposed dataset can be used for training, testing, and validation of soybean classification or reorganization models.
Files
Steps to reproduce
The dataset provided includes photographs taken from the Soybean agriculture field at Jabalpur under natural lighting conditions. These images were directly captured from the field and are stored in the raw dataset folder. The dataset consists of both healthy and diseased soybean images. The images were captured using a Nikon L810 camera and a Motorola g40 mobile camera. To preprocess the images, a Python script was employed, resulting in the creation of a separate dataset called the pre-processing dataset. The pre-processing dataset contains both healthy and diseased images, with a resolution of 256x256 pixels and in grayscale format.