datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
white-cabbage-leaf-damage
White Cabbage Leaf Damage Dataset
Description
This dataset contains images of white cabbage leaves with various types of damage. It is designed for researchers and developers working on agricultural computer vision and plant pathology detection.
Dataset Structure
The dataset is organized into folders representing different classes of leaf damage or healthy states.
Usage
You can use this dataset with the datasets library:
from datasets… See the full description on the dataset page: https://huggingface.co/datasets/Arko007/white-cabbage-leaf-damage.LeafNetThe PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification. However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large-scale dataset designed to support foundation models for leaf disease diagnosis. We introduce LeafNet comprises over 186,000 images from 22 crop species, covering 43 fungal diseases, 8 bacterial diseases, 2 mould (oomycete) diseases, 6… See the full description on the dataset page: https://huggingface.co/datasets/enalis/LeafNet.Dhan-Shomadhan_rice_leaf_disease_classification
Dhan Shomadhan Rice Leaf Disease Classification
This dataset provides real-world RGB images of rice leaves affected by multiple disease symptoms, captured under natural agricultural conditions. It offers a practical resource for developing and evaluating computer vision models in agricultural disease detection and monitoring applications. The dataset contains 1,106 images across 8 classes: Brown Spot, Browon Spot, Leaf Scaled, Rice Blast, Rice Tungro, Rice Turgro, Shath Blight… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/Dhan-Shomadhan_rice_leaf_disease_classification.tomato-leaf-disease-imagebean_cowpea_leaf_disease_classification
Bean Cowpea Leaf Disease Classification
A dataset for disease classification of bean and cowpea leaves. The dataset contains 4,467 images across 6 classes: Bacterial wilt, Blight, Fresh Leaf, Mosaic Virus, Rust, Septoria leaf spot.Images per class:
Bacterial wilt: 581
Blight: 510
Fresh Leaf: 1,090
Mosaic Virus: 1,141
Rust: 568
Septoria leaf spot: 577
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/bean_cowpea_leaf_disease_classification.chitrak_leaf_disease_classification
Chitrak Leaf Disease Classification
A dataset for disease classification of Chitrak Leaves. The dataset contains 10,660 images across 3 classes: dried, healthy, and unhealthy.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{patil2024plumbago,
title={Plumbago Zeylanica (Chitrak) leaf image dataset: a comprehensive collection for botanical studies, herbal medicine research, and environmental… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/chitrak_leaf_disease_classification.three_plant_leaf_disease_classification
Three Plant Leaf Disease Classification
A dataset for disease classification of three plant leaves: Aegle Marmelos, Hog Plum, and Lemon. The dataset contains raw and augmented versions.The raw dataset contains 3,941 images.Images per class:
Anthracnose: 360
Caterpillars_Infestation: 429
Cercospora_Leaf: 386
Citrus: 302
Healthy_Leaf: 674
Heathy_Leaf: 407
Leaf_Curl: 678
Leaf_Spot: 370
Sooty_Mold: 335
The augmented dataset contains 12,295 images.Images per class:
Anthracnose: 1… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/three_plant_leaf_disease_classification.Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset
Durian Plantation Disease Leaf Rot Detection Image Dataset
Ensures high-quality data through multiple rounds of annotation and automated consistency checks, combined with reviews by agricultural pathology experts. The annotation team consists of 10 professionals in agriculture and computer vision. Pre-processing steps include noise reduction, size adjustments, and color normalization to enhance the model's recognition capabilities. Data is stored in JPG format, organized… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset.tomato-leaf-disease-imageLeaf-Health-Status-Classification-Dataset
Leaf Health Status Classification Dataset
The current agricultural industry faces challenges in pest and disease monitoring and management, especially in large-scale plantations, where manual inspection is costly and inefficient. The application of existing machine vision technology in target detection and classification is not yet widespread, leading to an inability to promptly respond to pest invasions. This dataset aims to provide high-quality leaf health status classification… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Leaf-Health-Status-Classification-Dataset.Crop-Leaf-Damage-Detection-Dataset
Crop Leaf Damage Detection Dataset
The agriculture sector currently faces challenges in crop yield and quality due to diseases and pests, especially with the intensification of climate change. Farmers require effective monitoring tools. Existing monitoring solutions largely rely on manual inspections, which are time-consuming and prone to errors. This dataset aims to provide high-quality images of leaf damage to help AI models better identify and monitor crop health. Data collection… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Crop-Leaf-Damage-Detection-Dataset.Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset
Durian Plantation Disease Leaf Rot Detection Image Dataset
Ensures high-quality data through multiple rounds of annotation and automated consistency checks, combined with reviews by agricultural pathology experts. The annotation team consists of 10 professionals in agriculture and computer vision. Pre-processing steps include noise reduction, size adjustments, and color normalization to enhance the model's recognition capabilities. Data is stored in JPG format, organized… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Durian-Plantation-Disease-Leaf-Rot-Detection-Image-Dataset.tomato-leaf-disease-image