What is the Ghana Crop Disease Detection Dataset?

The Ghana Crop Disease Detection Dataset contains annotated images of crops like corn, pepper, and tomato from various Ghanaian regions. It supports AI model training for detecting diseases, pests, and crop health conditions.

How can this dataset be used in AI and machine learning?

This dataset is used to train and validate machine learning models for plant disease classification, pest detection, and crop yield prediction within precision agriculture systems.

Does the dataset include diverse agricultural conditions?

Yes. The dataset includes diverse crop conditions from multiple Ghanaian regions, varying climates, and soil types to ensure robust model training and generalization.

Does GTS.AI offer custom agricultural datasets?

Yes. GTS.AI provides custom dataset collection and annotation based on crop type, disease focus, and regional specifications, with options for bounding box, polygon, and semantic labeling.

How does GTS.AI ensure dataset quality and compliance?

All data undergo multi-layer manual QC, accuracy audits, and cleaning. GTS.AI operates under ISO 9001:2015 and ISO 27001:2013 standards, ensuring GDPR and HIPAA compliance for secure and ethical data handling.

Ghana Crop Disease Detection Dataset

Home » Dataset Download » Ghana Crop Disease Detection Dataset

Ghana Crop Disease Detection Dataset

Datasets

File

Ghana Crop Disease Detection Dataset

Use Case

Ghana Crop Disease Detection Dataset

Description

Explore the Ghana Crop Disease Detection dataset, featuring annotated images of corn, pepper, and tomato crops.

Description:

This dataset is designed to advance crop disease detection through machine learning and computer vision, featuring high-quality images of corn, pepper, and tomato crops from farms in Ghana. The dataset is ideal for developing AI models to identify and classify crop diseases in agricultural settings.

Download Dataset

The training set includes annotated images with bounding boxes that mark the location and type of crop diseases, allowing for the detection of multiple diseases in a single image. Each disease is carefully annotated to provide precise training data for machine learning models.

The test set contains images that may feature new, previously unseen diseases, with bounding boxes removed, making it perfect for testing model robustness and generalization to novel conditions.

Key Features:

Corn, Pepper, and Tomato Crops: Focused on three major Ghanaian crops.
Disease Annotations: Precise bounding boxes indicating disease presence and type.
Multi-Disease Detection: Training images may contain multiple diseases, annotated separately.
Test Set for Generalization: Test images may include new diseases not seen in training data.

Contact Us

Let's Discuss your Data collection Requirement With Us

To get a detailed estimation of requirements please reach us.

Ghana Crop Disease Detection Dataset