datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Crop_Weed_classification
Crop & Weed Classification Dataset
© 2026 Rishi. All rights reserved.
This dataset is provided under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
Usage
You are free to share and adapt this dataset under the following terms:
Attribution: You must give appropriate credit to the author.
Non-Commercial: You may not use this material for commercial purposes.
Citation & Paper
A research paper utilizing this… See the full description on the dataset page: https://huggingface.co/datasets/Rishi210904/Crop_Weed_classification.crop_pest_disease_classification
Crop Pest Disease Classification
A dataset for classification of disease/damage from common crop pests. The dataset contains raw and augmented versions.The raw dataset contains 25,170 images.Images per class:
anthracnose: 1,729
bacterial blight: 2,614
brown spot: 1,481
fall armyworm: 285
grasshoper: 673
green mite: 1,015
gumosis: 392
healthy: 3,235
leaf beetle: 938
leaf blight: 2,292
leaf curl: 514
leaf miner: 1,378
leaf spot: 1,249
mosaic: 1,205
red rust: 1,682
septoria leaf… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/crop_pest_disease_classification.early-crop-weed_classification
Early Crop Weed Classification
This dataset contains real-world RGB images capturing early-stage crop and weed plants in agricultural field environments. The images were collected under natural outdoor conditions, providing visual data for distinguishing between different plant species during critical growth phases. The dataset contains 508 images across 4 classes: black_nightsade, cotton, tomato, velvet_leaf .Images per class:
black_nightsade: 123
cotton: 54
tomato: 201… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/early-crop-weed_classification.greenhouse_crop_weed_classification
Greenhouse Crop Weed Classification
A dataset for classification of crops and weeds in a greenhouse setting. The dataset contains 28,000 images across 14 classes:Images per class:
Blackbean: 2,000
Canola: 2,000
Corn: 2,000
Field Pea: 2,000
Flax: 2,000
Horseweed: 2,000
Kochia: 2,000
Lentil: 2,000
Palmer Amaranth: 2,000
Ragweed: 2,000
Redroot Pigweed: 2,000
Soybean: 2,000
Sugar beet: 2,000
Waterhemp: 2,000
This dataset is indexed on https://project-agml.github.io/ as part of the… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/greenhouse_crop_weed_classification.Crop-Pest-and-Disease-Classification-Dataset
Crop Pest and Disease Classification Dataset
The current agricultural industry faces severe threats to crop yield and quality from pests and diseases. Traditional manual identification methods are inefficient and prone to errors. Existing solutions mostly rely on expert experience, lacking systematic and efficient approaches. This dataset aims to assist in the development of efficient AI models for automatic pest and disease classification by providing diverse crop pest and disease… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Crop-Pest-and-Disease-Classification-Dataset.Crop-Flood-Disaster-Classification-Dataset
Crop Flood Disaster Classification Dataset
The current agricultural industry faces challenges of frequent flood disasters, making it difficult to quickly assess crop damage, affecting the stability of agricultural production and supply chains. Existing solutions largely rely on manual assessments which are inefficient and highly subjective, failing to meet the need for rapid response. This dataset aims to help AI models quickly assess damage by providing images of crops with… See the full description on the dataset page: https://huggingface.co/datasets/Harini1617/Crop-Flood-Disaster-Classification-Dataset.Crop-Flood-Disaster-Classification-Dataset
Crop Flood Disaster Classification Dataset
The current agricultural industry faces challenges of frequent flood disasters, making it difficult to quickly assess crop damage, affecting the stability of agricultural production and supply chains. Existing solutions largely rely on manual assessments which are inefficient and highly subjective, failing to meet the need for rapid response. This dataset aims to help AI models quickly assess damage by providing images of crops with varying… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Crop-Flood-Disaster-Classification-Dataset.