@misc{10481/95798,
year = {2024},
month = {9},
url = {https://hdl.handle.net/10481/95798},
abstract = {Accurately and automatically estimating vineyard yield is a significant challenge. This study focuses on grape
bunch counting in commercial vineyards using advanced deep learning techniques and object detection algorithms.
The aim is to overcome the limitations of conventional yield estimation techniques, which are labour
intensive, costly, and often inaccurate due to the spatial and temporal variability of the vineyard. This research
proposes a non-invasive methodology for identifying grape bunches under different occlusion conditions using
RGB cameras and deep learning models. The methodology is based on the collection of RGB images captured
under field conditions, coupled with the implementation of the YOLOv4 architecture for data processing and
analysis. Statistical indicators were used to evaluate the performance of the developed models. The comprehensive
model produced a favourable outcome during validation, with an error rate of 1.12 bunches (R2 = 0.83).
In the test dataset, the model achieved an error rate of 1.12 (R2 = 0.81). The results highlight the potential of
emerging technologies to significantly improve vineyard yield estimation. This approach has the potential to
assist vineyard management practices, enabling more informed and efficient decisions that could increase both
the quantity and quality of grape production intended for winemaking.},
organization = {FPI PhD grants 591/2021 and 1150/2020 by Universidad de La Rioja and Gobierno de La Rioja},
publisher = {Elsevier},
keywords = {Artificial intelligence},
keywords = {Yield estimation},
keywords = {YOLO},
title = {Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions},
doi = {10.1016/j.compag.2024.109421},
author = {Íñiguez, Rubén and Gutiérrez Salcedo, Salvador and Poblete Echeverría, Carlos and Hernández, Inés and Barrio, Ignacio and Tardaguila, Javier},
}