LitchiLeaf4001: A Comprehensive Dataset of Lychee Leaf Diseases for AI-Based Visual Diagnosis
Description
LitchiLeaf4001 is a curated image dataset focused exclusively on lychee (Litchi chinensis) leaves. It was collected to support research and development in computer vision, machine learning, and deep learning for plant disease detection. This dataset is designed to empower the agricultural AI community, particularly in Bangladesh, where lychee is a commercially important fruit crop. The dataset consists of 4,001 images captured from lychee orchards in three agriculturally diverse districts: Dhaka, Manikganj, and Gaibandha, between December 2024 and April 2025. It includes both healthy and diseased leaves affected by common visual conditions found in lychee plants. Class Distribution: 1. Anthrax – 620 images 2. Curly Leaf – 368 images 3. Dried Leaf – 555 images 4. Healthy Leaf – 662 images 5. Insect Hole – 1,157 images 6. Yellow Mosaic Virus – 639 images. Location: 1. Dhaka: [Latitude (°N): 23.8103, Longitude (°E): 90.4125] 2. Manikganj: [Latitude (°N): 23.8617, Longitude (°E): 89.9333] 3. Gaibandha: [Latitude (°N): 25.3287, Longitude (°E): 89.5284] Potential Applications: 1. Computer Vision: - Disease region detection and segmentation - Leaf health visual feature extraction - Real-time visual monitoring of lychee trees 2. Machine Learning: - Multiclass classification of lychee leaf diseases - Development of explainable AI (XAI) models for plant health assessment - Decision support tools for agricultural advisors 3. Deep Learning: - CNN-based disease recognition (e.g., InceptionV3, ResNet, MobileNet) - Attention-based DL models for fine-grained disease spotting - Integration with GANs for synthetic data augmentation 4. Smart Agriculture / AgriTech: - Mobile-based plant disease diagnostic apps - IoT-integrated crop monitoring systems - Early warning systems for lychee disease outbreaks