Marconi Lab Bean Crop Disease Diagnosis

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Marconi Lab Bean Crop Disease Diagnosis

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Marconi Lab Bean Crop Disease Diagnosis

Use Case

Marconi Lab Bean Crop Disease Diagnosis

Description

Explore a curated subset of the Makerere University Beans Image Dataset for diagnosing bean crop diseases. This dataset includes images categorized into healthy, ALS, Bean Rust, and unknown classes, supporting classification and detection tasks. Ideal for real-time disease identification using edge computing and deep learning

Description:

This dataset is a curated subset of the Makerere University Beans Image Dataset, specifically designed for diagnosing diseases in bean crops and conducting spatial analysis. It includes images categorized into four classes: healthy bean leaves, Angular Leaf Spot (ALS) in bean leaves, Bean Rust in bean leaves, and an additional ‘unknown’ class. These images are crucial for classifying and detecting various bean leaf conditions, aiding in the identification of target bean leaf classes and distinguishing them from other visuals. The dataset was employed in a project utilizing edge computing and deep learning for real-time identification of bean plant pathologies.

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Classification Task

The dataset contains images categorized into four classes: healthy, ALS, Bean Rust, and an unknown class. These labeled images are used for training and evaluating classification models to diagnose bean leaf conditions.

Detection Task

For the detection task, the dataset includes annotated images for ALS and Bean Rust, with bounding boxes marking affected areas. Unlabeled healthy images are also provided to enhance detection model learning.

Application

This dataset supports real-time disease identification in bean crops, leveraging edge computing and deep learning for improved crop management and increased agricultural productivity.