Fruit detection and 3D location using instance segmentation neural networks and SfM photogrammetry

Abstract

The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the effi-ciency of agriculture management. Most of the current fruit detec-tion systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location, combining the use of instance segmentation neu-ral networks and Structure-from-Motion (SfM) photogrammetry.

This work was partly funded by the Secretaria d’Universitats i Recerca de la Gener-alitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (projects AGL2013-48297-C2-2-R and TEC2016-75976-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355).