Thursday 27 March 2025
Scientists have made a significant breakthrough in developing an AI-powered system that can detect and count palm trees using unmanned aerial vehicles (UAVs). The system, called PRISM, has been tested on a large dataset of images taken over western Ecuador’s ecologically diverse reserves.
Palm trees are not only iconic symbols of tropical landscapes but also play a crucial role in supporting local economies and global forest product supply chains. However, detecting these tall, slender plants in dense forests can be challenging due to overlapping crowns, uneven shading, and heterogeneous landscapes.
PRISM uses a combination of machine learning algorithms and computer vision techniques to identify palm trees from high-resolution images taken by UAVs. The system first constructs an orthomosaic image, which is a large composite image created from thousands of aerial images. This allows researchers to pinpoint the exact location of each palm tree.
The next step involves using object detection algorithms to identify potential palm trees in the image. These algorithms analyze various features such as shape, size, and texture to determine whether an object is indeed a palm tree or not.
Once a palm tree has been detected, PRISM refines its segmentation by refining the bounding box around the object. This step helps to eliminate any confusion caused by nearby objects or shadows.
The final stage involves calibrating the confidence scores of each detection against the intersection over union (IoU) metric. IoU measures the overlap between the predicted bounding box and the ground truth bounding box, providing a more accurate assessment of the system’s performance.
PRISM has been tested on a dataset of 21 ecologically diverse sites in western Ecuador, with a total of 8,830 bounding boxes and 5,026 palm center points. The results show that PRISM is capable of detecting palm trees with high accuracy, even in challenging environments.
One of the key benefits of PRISM is its ability to adapt to different palm species and environments. This means that the system can be easily applied to other regions or ecosystems where palms are present.
The development of PRISM has significant implications for conservation efforts and sustainable forest management. By providing a reliable method for detecting and counting palm trees, researchers can better understand the ecological importance of these plants and develop targeted strategies for their conservation.
In addition, PRISM’s ability to analyze large datasets quickly and accurately makes it an ideal tool for monitoring changes in palm tree populations over time. This information can help policymakers make informed decisions about forest management and conservation policies.
Cite this article: “PRISM: AI-Powered System Detects Palm Trees with High Accuracy”, The Science Archive, 2025.
Ai, Palm Trees, Uavs, Machine Learning, Computer Vision, Object Detection, Segmentation, Bounding Box, Iou, Conservation, Sustainability
