@misc{10481/106154,
year = {2025},
month = {7},
url = {https://hdl.handle.net/10481/106154},
abstract = {Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since
grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation
for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages.
This study presents a deep learning-based approach for detecting grapevine inflorescences
and bunches during early development, assessing how phenological stage and illumination
conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological
stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time
illumination), and six grapevine cultivars. All images were manually annotated following
a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining
all scenarios. The results show that the fruit-set stage under daylight provided the best
performance (F1 = 0.77, R2 = 0.97), while for inflorescences, night-time imaging yielded the
most accurate results (F1 = 0.71, R2 = 0.76), confirming the benefits of artificial lighting in
early stages. These findings define optimal scenarios for early-stage organ detection and
support the integration of automated detection models into vineyard management systems.
Future work will address scalability and robustness under diverse conditions.},
organization = {Universidad de La Rioja - Gobierno de La Rioja (FPI Grant 591/2021)},
publisher = {MDPI},
keywords = {yield prediction},
keywords = {inflorescence},
keywords = {grape bunch},
keywords = {precision viticulture},
keywords = {deep learning},
title = {Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning},
doi = {10.3390/agriculture15141495},
author = {Íñiguez, Rubén and Poblete-Echeverría, Carlos and Barrio, Ignacio and Hernández, Inés and Gutiérrez-Salcedo, Salvador and Martínez-Cámara, Eduardo and Tardaguila, Javier},
}