Industrial hemp (Cannabis sativa L.) is a multipurpose crop with growing interest due to its environmental adaptability, low input requirements, and potential contribution to sustainable agricultural systems. This study evaluated the agronomic performance of four industrial hemp varieties grown under the edaphoclimatic conditions of the Alentejo region over two consecutive growing seasons (2024 and 2025) using different sowing dates. Phenological stages, plant height and growth parameters were monitored, complemented by meteorological data obtained from IPMA. The results revealed clear differences between years. The later sowing date in 2024 promoted greater vegetative growth, resulting in taller plants, while the earlier sowing in 2025 extended the vegetative phase and delayed flowering. Varietal differences were also observed, particularly for Fibror 79, which flowered slightly later, suggesting greater photoperiod sensitivity. These patterns confirm that both thermal environment and sowing date play a decisive role in hemp phenological development. The findings also highlight the high plasticity of the crop, which demonstrated strong adaptation to the hot and dry Mediterranean summers. Overall, appropriate selection of variety and sowing date can optimize vegetative and reproductive development, representing an important strategy for sustainable agricultural systems in the Alentejo region.

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture.