The advancement of technology in agriculture has driven the use of drones for spraying, with their increasing adoption presenting challenges in calibration and volumetric distribution efficiency. This study aimed to evaluate the volumetric distribution of drone spraying by combining different operational parameters to determine spray swath and application uniformity. Experiments were conducted using a DJI T10 drone and a volumetric distribution table to assess the impact of different flight heights (2, 3, and 4 m), application volumes (8, 12, 16, and 20 L ha⁻¹), and nozzle types (FV 110 015, FL 110 010, and CO 080 010). Environmental conditions were monitored, and data were analyzed using histograms, analysis of variance (ANOVA) by F-test (p ≤ 0.05), and the Scott–Knott test (p ≤ 0.05) to group means. Results indicated that a lower application volume (8 L ha⁻¹) led to greater application uniformity and a narrower spray swath. Higher flight altitude (4 m) resulted in a wider spray swath and a normal distribution of spray deposition. Fine droplet nozzles (CO 080 010) enhanced uniformity, while very coarse droplets (FV 110 015) concentrated more volume in the center of the swath. Thus, using fine droplet nozzles (CO 080 010), lower application volume (8 and 12 L ha⁻¹), and higher flight altitude (4 m) as operational parameters maximizes drone spraying efficiency; however, this also increases drift potential.

The production of “winter wines” in south-eastern Brazil, enabled by the double pruning technique, requires precise assessment of grape technological maturity to ensure high-quality outputs. However, conventional monitoring approaches are destructive, labor-intensive, and limited in their ability to capture vineyard spatial variability. This study aimed to develop and validate a non-destructive predictive framework for Soluble Solids (°Brix) and Titratable Acidity (TA) by integrating spatial remote sensing data with temporal agrometeorological information. Multispectral imagery was acquired via an unmanned aerial vehicle in a vineyard cultivated with Sauvignon Blanc and Syrah, from which vegetation indices were derived and combined with Growing Degree-Days to train machine learning models, including Random Forest, Multilayer Perceptron, and XGBoost. The incorporation of agrometeorological data significantly improved predictive performance compared to models based solely on vegetation indices. Among the tested algorithms, XGBoost achieved the highest accuracy, with coefficients of determination of 0.89 for °Brix and 0.77 for TA, achieved by XGBoost on an independent hold-out test set. Model interpretability analysis indicated that Growing Degree-Days and cultivar were the primary drivers of maturation dynamics, while vegetation indices refined predictions by accounting for spatial variability in plant vigor. Overall, the proposed approach represents a promising proof-of-concept framework for non-destructive maturity monitoring in precision viticulture, supporting improved monitoring of grape maturation. However, multi-season validation across diverse vineyard conditions is required to confirm its generalizability and support its application as a routine decision-support tool.