Cauliflower Leaf Diseases: A Computer Vision Dataset for Smart Agriculture

The Cauliflower Leaf Disease Dataset is a curated collection of high-quality images designed for machine learning and deep learning applications in plant disease detection. The dataset comprises 2,661 images categorized into three classes: Healthy (934), Insect Hole (639), and Black Rot (1,088). The images are collected under varying lighting conditions and angles to enhance model generalization.

Key Features:
Healthy Leaves (934): Images of fresh, disease-free cauliflower leaves.
Insect Hole (639): Leaves showing visible insect damage, such as holes caused by pests.
Black Rot (1,088): Leaves affected by Xanthomonas campestris pv. campestris, a bacterial infection causing blackened veins and necrotic lesions.

Applications:
Computer Vision: Image segmentation, feature extraction, and object detection for plant pathology studies.
Machine Learning: Traditional classifiers (SVM, Random Forest) and feature engineering techniques for automated classification.
Deep Learning: Convolutional Neural Networks (CNNs), Transfer Learning (ResNet, VGG, EfficientNet), and Explainable AI (Grad-CAM) to identify disease patterns.
Agricultural Decision Support: Real-time disease monitoring, precision farming applications, and smartphone-based diagnosis for farmers.

This dataset is a crucial resource for researchers working on AI-driven plant disease identification and can contribute to the advancement of precision agriculture and sustainable farming solutions.