Apple detection method based on fusion of infrared thermal image and visible-light image

Abstract

To address the challenges posed by lighting variations and fruit occlusion in open orchard environments, which significantly affect the performance of apple-harvesting robots, this study proposes an apple detection method based on the fusion of infrared thermal images and visible-light images. Firstly, An edge feature-based registration technique was employed to achieve precise alignment of infrared and visible-light images. Subsequently, an improved YOLOv8s model integrated with the SeAFusion framework was utilized to facilitate efficient apple detection. Experimental results revealed that the proposed method achieved 94.9% mean accuracy and 89.5% mean recall across diverse illumination scenarios (normal/ strong/ backlight), surpassing visible-light-only detection by 0.6%, 4.8%, and 3.5% in apple count accuracy under respective conditions. The proposed method established a robust framework for vision-based harvesting robots, significantly improving operational reliability in complex orchard environments and providing technical foundations for scalable agricultural automation.