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

A new lightweight deep learning architecture named YOLO26-RipeLoc Lite has been proposed for the detection of ripe tomatoes and localization of picking points in greenhouse robotic harvesting. This model integrates a Lightweight Feature Pyramid Network, a Ripeness-Aware Attention Module, and a Compact Detection Head, enhancing the efficiency of automated harvesting processes.

The development of YOLO26-RipeLoc Lite is significant as it aims to improve the accuracy and efficiency of robotic harvesting in greenhouse environments, potentially increasing productivity and reducing labor costs in the agricultural sector, particularly in regions like Abu Dhabi, UAE.