Oil Palm Tree Detection for Anomaly Identification
Description
This dataset supports an advanced artificial vision system for detecting anomalies in oil palm (Elaeis guineensis) crops. It consists of RGB captured using a DJI Phantom 4 Multispectral UAV. The dataset is labeled into two main classes: 'PalmSan' (healthy palms) and 'PalmAnom' (anomalous palms). It was used to train and validate a Faster R-CNN with ResNet-50 FPN model, fine-tuned in PyTorch. The dataset plays a crucial role in high-accuracy classification for automated disease detection and stress assessment, contributing to scalable and sustainable precision agriculture solutions.
Files
Steps to reproduce
Drone Setup & Flight Planning Use a DJI Phantom 4 Multispectral drone. Plan the flight path ensuring full coverage of the target oil palm plantation. Conduct a preliminary field study to determine optimal flight parameters. Flight Execution Configure the drone to fly at a speed of 2.5 m/s. Set the flight altitude between 17 to 30 meters above ground level. Ensure the camera is perpendicular to the ground for consistent image capture. Data Preprocessing Resize all collected images to 800×600 pixels for uniformity. Rename images following the pattern 'imgPalmXX', where XX represents the image index. Annotation Process Upload the images to Roboflow Annotate. Use Roboflow's annotation tools to label images based on predefined classes ('PalmSan' for healthy and 'PalmAnom' for anomalous). Export the labeled dataset in the required format for model training.
Institutions
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Funding
National University Toribio Rodríguez de Mendoza