Cassava Leaf Image Dataset

Purpose

The dataset aims to support data scientists, researchers, and machine learning experts in improving cassava disease detection methods. The goal is to enhance disease identification in Uganda and potentially other cassava-growing regions by providing reliable data for training AI models.

Content

The dataset features images categorized into three main classes:

1. Cassava Mosaic Disease: Images showcasing leaves affected by this viral disease, characterized by yellow mosaic patterns on the leaves.
2. Cassava Brown Streak: Images displaying symptoms of this disease, including brown streaks and necrosis on leaves and stems.
3. Healthy Specimens: Images of healthy cassava leaves used as a control group for comparison.

Additional Features

• Real-World Application: Unlike previous datasets captured in controlled laboratory environments, this dataset includes images taken directly from the field, ensuring more practical and applicable data for real-world scenarios.
• High Quality and Accuracy: Each image is meticulously labeled to ensure the highest quality and accuracy, facilitating better training and validation of machine learning models.

Potential Use Cases

• Research and Development: Ideal for academic research, this dataset can aid in developing new methodologies and technologies for cassava disease detection.
• Agricultural Technology: Companies and organizations can leverage this dataset to create advanced tools and applications for farmers, improving disease management and crop yield.
• Global Impact: While focused on Uganda, the insights and models developed using this dataset can be adapted and applied to other cassava-producing regions worldwide.