An Advanced Photogrammetric Solution to Measure Apples

This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers' and agronomists' fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.

Automated summary

The study introduces an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. By utilizing smartphone-based videos and combining photogrammetry, deep learning, and geometric algorithms, the proposed approach aims to simplify fieldwork for farmers and agronomists while providing more accurate measurements and estimating harvesting dates. Experiment results demonstrate the accuracy and potential of the method, with data, images, code, and network weights available on the 3DOM-FBK GitHub account.