Dataset for Detecting Diseases in Sweet Orange Leaves

Abstract:
This dataset is designed for the detection and classification of common diseases in Sweet Orange leaves, specifically foliage damage and mealybug infestations. The dataset was curated to support advancements in agricultural disease detection through machine learning, providing a comprehensive resource for researchers.
Data Summary:
1. Raw Data:
o Total Samples: 3,569
o Disease Dataset: 2,660 samples, including:
 Foliage Damage: 1,200
 Mealybug Infestation: 560
o Fresh Dataset: 909 samples, including:
 Healthy Leaves: 864
 Medium Healthy Leaves: 45
2. Augmented Data:
o Total Samples: 21,409
o Disease Dataset: 15,955 samples, including:
 Foliage Damage: 12,596
 Mealybug Infestation: 3,359
o Fresh Dataset: 5,454 samples, including:
 Healthy Leaves: 5,184
 Medium Healthy Leaves: 270
Purpose:
The Sweet Orange Leaf Disease Detection Dataset is a versatile resource for machine learning applications in agriculture. It is particularly suited for training image-based models, such as Convolutional Neural Networks (CNNs), to:
• Identify leaves affected by diseases like foliage damage or mealybug infestation.
• Assess varying levels of healthiness in fresh leaves.
This dataset facilitates:
• Automated Disease Detection: Streamlining monitoring processes in Sweet Orange cultivation.
• Early Intervention Strategies: Supporting timely responses to prevent crop losses.
• Enhanced Model Development: Integrating image features (e.g., shape, color, texture) with environmental parameters like humidity and temperature to improve predictive accuracy.
By fostering precision agriculture practices, this dataset contributes to sustainable citrus farming and demonstrates the potential of AI-driven solutions for crop health management.