  Robust and reliable place recognition and loop closure detection in
agricultural environments is still an open problem. In particular, orchards are
a difficult case study due to structural similarity across the entire field. In
this work, we address the place recognition problem in orchards resorting to 3D
LiDAR data, which is considered a key modality for robustness. Hence, we
propose ORCHNet, a deep-learning-based approach that maps 3D-LiDAR scans to
global descriptors. Specifically, this work proposes a new global feature
aggregation approach, which fuses multiple aggregation methods into a robust
global descriptor. ORCHNet is evaluated on real-world data collected in
orchards, comprising data from the summer and autumn seasons. To assess the
robustness, we compare ORCHNet with state-of-the-art aggregation approaches on
data from the same season and across seasons. Moreover, we additionally
evaluate the proposed approach as part of a localization framework, where
ORCHNet is used as a loop closure detector. The empirical results indicate
that, on the place recognition task, ORCHNet outperforms the remaining
approaches, and is also more robust across seasons. As for the localization,
the edge cases where the path goes through the trees are solved when
integrating ORCHNet as a loop detector, showing the potential applicability of
the proposed approach in this task. The code will be publicly available
at:\url{https://github.com/Cybonic/ORCHNet.git}
