Abstract
The growing use of Unmanned Aerial Vehicles (UAVs) in agriculture has made data collection more efficient and cost-effective, enabling the development of advanced solutions to enhance agricultural productivity. In this work, we present a dataset of intercropped durian (durio zibethinus) and papaya (carica papaya) trees, extracted from aerial images captured by a UAV. A total of 311 UAV-captured images were collected over a mixed plantation, where durian trees, being taller and broader, contrast visually with the shorter and more slender papaya trees. The trees were subsequently labeled into two categories based on type and visual characteristics: durian trees (3327 images) and papaya trees (2872 images). This dataset serves as a valuable resource for multiple tree species classification, spatial pattern analysis, and decision-making in precision agriculture using machine learning and deep learning methods. Insights derived from this dataset can support improved orchard management, including crop inventory, health monitoring, and optimization of intercropping strategies.