Plant image classification dataset

Overview: This repository contains 1215 high-resolution RGB images captured by PSNC and WODR for the dataset specifically designed for training two models to recognize Cercospora Leaf in beet and Brown Rust of Rye in rye. The dataset has been utilized to train CNN models for product avaiable at AI4EOSC marketplace that are integrated into the mobile application of eDWIN advisory platform. Dataset Composition: The dataset is organized into separate directories for each plant and class: ├── beet/│ ├── unaffected/│ │ ├── 03007677-7ac3-4753-8785-b18141c5e116.jpg│ │ ├──147fdffc-ecd3-4b36-b08f-8dd2e0d0875f.jpg│ │ └── ... │ └── cercospora/│ ├── 023469c9-9c0f-4691-8d80-f2deb540af5c.jpg│ ├──071a839d-f590-4971-94e5-78183215bc26.jpg│ └── ...└── rye/ ├── unaffected/ │ ├── 0bhbfd34899f8sdsk.jpg │ ├── 098u98nhju9889fgf.jpg │ └── ... └── brown_rust/ ├──0gjnj3bj5hbjh54sab.jpg ├──b09difas90jnis0ndin.jpg └── ...Usage: The dataset is structured to aid in the development and testing of models for agricultural disease detection. The following table represents structure of dataset_overview.csv: Plant Class Filename beet unaffected 03007677-7ac3-4753-8785-b18141c5e116.jpg beet cercospora 023469c9-9c0f-4691-8d80-f2deb540af5c.jpg rye unaffected 0bhbfd34899f8sdsk.jpg rye brown_rust 0gjnj3bj5hbjh54sab.jpg Note: All data were recorded under various field conditions, and experts verified annotations to ensure the accuracy of labels. The dataset is continually updated with new data and annotations to enhance model robustness and accuracy.