LeAF Pest Detection Dataset

A large-scale dataset for training and evaluating computer vision models for agricultural pest detection and classification.

Aditya Sengupta, Ana Lucic, Rob Kooper, Vikram Adve

The LeAF Pest Detection Dataset is a comprehensive collection of images and annotations designed to facilitate the development and evaluation of robust computer vision models for identifying and classifying agricultural pests. The dataset features:

Object Detection and Classification: Images are annotated with bounding boxes around pests, along with corresponding class labels.
Large Number of Classes: The dataset includes 3,580 distinct agricultural pest classes, representing a wide variety of pests affecting various crops.
Large Number of Images The dataset contains 35,982 images.
High-Quality Images: Images were sourced from iNaturalist, a citizen science platform, ensuring a diverse range of real-world conditions, including variations in lighting, background, and pest appearance.
Data Splits: The dataset is divided into training, validation, and testing sets to support standardized model development and evaluation.
Annotation Format: Annotations are provided in standard YOLO format. Each image has a corresponding text file containing bounding box coordinates (normalized x_center, y_center, width, height) and class IDs.

This dataset is intended to promote research and development in precision agriculture, specifically in the area of automated pest monitoring and management.

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