Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning

Rubén Íñiguez, Carlos Poblete-Echeverría, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Eduardo Martínez-Cámara and Javier Tardáguila ()
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Rubén Íñiguez: Televitis Research Group, University of La Rioja, 26006 Logroño, Spain
Carlos Poblete-Echeverría: South African Grape and Wine Research Institute (SAGWRI), Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
Ignacio Barrio: Televitis Research Group, University of La Rioja, 26006 Logroño, Spain
Inés Hernández: Televitis Research Group, University of La Rioja, 26006 Logroño, Spain
Salvador Gutiérrez: Department of Computer Science and Artificial Intelligence (DECSAI), Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada (UGR), 18014 Granada, Spain
Eduardo Martínez-Cámara: Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, Spain
Javier Tardáguila: Televitis Research Group, University of La Rioja, 26006 Logroño, Spain

Agriculture, 2025, vol. 15, issue 14, 1-23

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, R 2 = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R 2 = 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.

Keywords: yield prediction; inflorescence; grape bunch; precision viticulture; deep learning; object detection; yolov11 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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