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GCC AI Research

Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

arXiv · · Notable

CV Research Robotics Arabic AI Agriculture

Summary

Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.

Keywords

palm tree · counting · geolocation · aerial images · deep learning

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YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting

arXiv ·

YOLO26-RipeLoc Lite is a new lightweight deep learning architecture designed for simultaneous detection, ripeness classification, and center-point localization of greenhouse tomatoes for robotic harvesting. The model incorporates a Lightweight Feature Pyramid Network, a Ripeness-Aware Attention Module, and a Compact Detection Head for efficient and precise operation. Evaluated on a custom dataset from the SILAL greenhouse in Abu Dhabi, UAE, it achieved a [email protected] of 92.9% with only 2.38 million parameters, outperforming existing YOLO models in accuracy-efficiency. Why it matters: This research provides an efficient and accurate solution for automating a critical agricultural process, enhancing food security and technological capabilities in the region's greenhouse farming.