Integrating Orthophotos and Field Data for Precision Vineyard Yield Prediction: A Case Study of Tempranillo Grapevines

Anticipating the response of grapevines to environmental variability is crucial for opti-mizing field management practices. This study explores the interaction between vines and their habitat across the growing cycle to inform more effective vineyard management. The research was conducted at the "Alhambra" plot in Ciudad Real (38.8089720, -3.0705830), which spans approximately 6 hectares of irrigated Tempranillo (Vitis vinifera L.) vines. Vine spacing is 3.05x1.54 m², and the training system is a double guyot pruned, vertical shoot positioning. The study utilizes data collected over 2024.

Within the plot, three replicates of 30 plants each were sampled. Measurements were taken from consecutive rows, 15 plants each. At the phenological stage of separated clus-ters, the number of clusters was recorded, while berry weight and the number of berries per cluster were assessed at veraison and harvest. Yield partitioning was determined at harvest. Additional parameters were also measured, including total soluble solids, surface area, pruning and shoot weight.

A custom script was developed to analyze the orthophotos of the vineyard to quantify the trellis length occupied by vines, excluding gaps where vines were missing. This method enables precise calculation of the vine-covered productive area. By combining these or-thophoto analyses with field-estimated yields per linear meter of vine, the study could provide accurate vineyard yield predictions. The accuracy and effectiveness of this inte-grated methodology are thoroughly evaluated.

Acknowledgements BigPrediData