Keras-Seedlings Dataset

Keras-Seedlings Dataset


Keras-Seedlings Dataset


Keras-Seedlings Dataset

Use Case

Computer Vision


Explore the comprehensive dataset featuring segmented images of weed and crop seedlings, specifically designed for machine learning classification tasks. This dataset provides a diverse collection of high-quality images, each carefully segmented to highlight the distinct characteristics of weed and crop seedlings.

Keras-Seedlings Dataset


This dataset, originally used in a Kaggle competition, is a specialized collection prepared by the Signal Processing Group of the Department of Engineering at Aarhus University. It has been meticulously curated to facilitate seamless integration into machine learning projects, specifically designed for use with image data generators.

Dataset Description:

This comprehensive dataset features high-quality images of various weed and crop seedlings, making it ideal for classification tasks. Each image has been expertly segmented to display only a single seedling, ensuring clear and focused data for accurate model training. This meticulous segmentation allows researchers to concentrate on the unique features of each seedling type without distractions from other plants or background noise.

The primary objective of this dataset is to enable the development of robust classifiers that can accurately distinguish between different types of plant seedlings based on their images. This capability is particularly important in agricultural technology, where precise identification of weeds and crops can significantly enhance farming practices. By accurately classifying seedlings, farmers can implement more effective weed control strategies and optimize crop management, ultimately leading to increased yields and reduced use of herbicides.

Moreover, the dataset’s high-resolution images provide detailed visual information, which is crucial for training advanced machine learning models. These models can learn to recognize subtle differences in seedling appearance, such as leaf shape, texture, and color, which are essential for distinguishing between species. The clarity and quality of the images ensure that even the smallest features are captured, allowing for precise and reliable classification.


We extend our heartfelt gratitude to the Aarhus University Department of Engineering Signal Processing Group for their generosity in publishing and sharing this valuable dataset with the academic and research community.

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