The sugarcane crop is of great economic relevance to Brazil, and the precise productivity estimation is a major challenge in production. Therefore, the aim of this study was to estimate the productivity of sugarcane cultivars in different regions, using multispectral sensors embedded in RPAs and biometric variables sampled in the field. The study was conducted in two experimental areas, located in the municipalities of Itirapina-SP and Iracemápolis-SP, with 16 cultivars in a randomized block design. The images were acquired using the multispectral sensor MicaSense Altum, allowing the extraction of spectral bands and vegetation indices. In parallel, biometric variables were collected at 149 and 295 days after planting (DAP). The machine learning models Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were calibrated using different sets of variables, and, despite the similar performance, it was decided to use the model derived from XGBoost in the analyses, since it deals more effectively with overfitting. The results indicated a good performance of the model (R² = 0.83 and 0.66; RMSE = 18.7 t ha⁻¹ and 25.3 t ha⁻¹; MAE = 15.7 and 20.2; RPIQ = 3.22 and 2.61) for the validations K-fold and Leave-one-out cross-validation (LOOCV). The correlations between biometric variables, spectral bands, and vegetation indices varied according to crop development stage. The leaf insertion angle presented a strong correlation with near-infrared (NIR) (r = 0.76) and the indices ExG and VARI (r = 0.70 and r = 0.69, respectively). The present work demonstrated that the integration between multispectral and biometric data represents a promising approach for estimating sugarcane productivity.

Pastures are the main feed source for beef cattle production, a sector in which Brazil plays a prominent global role. This study aimed to develop predictive models for dry matter yield (DM yield, kg ha⁻¹), crude protein (CP), and neutral detergent fiber (NDF) in Urochloa brizantha tropical pastures using laboratory VIS-NIR-SWIR spectroscopy, and to identify spectral patterns associated with these variables. Samples were collected from a commercial pasture area of approximately 200 ha, subdivided into 19 paddocks cultivated with Urochloa brizantha cv. Marandu and managed under rotational grazing during 2023. Forage samples were oven-dried, ground, and spectrally measured using a FieldSpec spectroradiometer (350–2500 nm). Partial least squares regression (PLSR) models were calibrated and evaluated using cross-validation, and informative wavelengths were identified using Variable Importance in Projection (VIP) scores. DM variability was mainly associated with near-infrared regions, CP with visible and near-infrared regions, and NDF with the visible region. Models calibrated with VIP-selected wavelengths achieved acceptable performance for CP (R²CV = 0.74) and NDF (R²CV = 0.72), whereas the general full-spectrum models showed moderate performance for CP (R²CV = 0.57) and acceptable performance for NDF (R²CV = 0.75). Temporal transferability varied among sampling periods, with greater robustness for CP and NDF than for DM. Overall, DM prediction remained limited and showed poor temporal transferability.