The crop water stress index (CWSI) is a widely used analytical tool based on portable thermography. This method can be useful in replacing the traditional stem water potential method obtained with a Scholander chamber (PMS Model 600) because the latter is not feasible for large-scale studies due to the time involved and the fact that it is invasive and can cause damage to the plant. The present work had three objectives: (i) to understand if CWSI estimated using an aerial sensor can estimate the water status of the plant; (ii) to compare CWSI from aerial-thermographic and portable thermal cameras with stem water potential; (iii) to estimate the capacity of an unmanned aerial vehicle (UAV) to calculate and spatialize CWSI. Monitoring of CWSI (CWSI_P) using a portable device was performed directly in the canopy, by measuring reference temperatures (T_dry, T_wet, and canopy temperature (T_c)). Aerial CWSI calculation was performed using two models: (i) a simplified CWSI model (CWSI_S), where the T_dry and T_wet were estimated as the average of 1% of the extreme temperature, and (ii) an air temperature model (CWSI_Tair) where air temperatures (T_air + 7 °C) were recorded as T_dry and in the T_wet, considering the average of the lowest 33% of histogram values. In these two models, the T_c value corresponded to the temperature value in each pixel of the aerial thermal image. The results show that it was possible to estimate CWSI by calculating canopy temperatures and spatializing CWSI using aerial thermography. Of the two models, it was found that for CWSI_Tair, CWSI_S (R² = 0.55) evaluated crop water stress better than stem water potential. The CWSI_S had good correlation compared with the portable sensor (R² = 0.58), and its application in field measurements is possible.

Vineyard irrigation management in temperate zones requires knowledge of the crop water requirements, especially in the context of climate change. The main objective of this work was to estimate the crop evapotranspiration (ET_c) of Vitis vinifera cv. Loureiro for local conditions, applying the dual crop coefficient approach. The study was carried out in a vineyard during two growing seasons (2019–2020). Three irrigation treatments, full irrigation (FI), deficit irrigation (DI), and rainfed (R), were considered. The ET_c was estimated using the SIMDualKc model, which performs the soil water balance with the dual Kc approach. This balance was performed by calculating the basal coefficients for the grapevine (K_{cb crop}) and the active soil ground cover (K_{cb gcover}), which represent the transpiration component of ET_c and the soil evaporation coefficient (K_e). The model was calibrated and validated by comparing the simulated soil water content (SWC) with the soil water content data measured with frequency domain reflectometry (FDR). A suitable adjustment between the simulated and observed SWC was obtained for the 2019 R strategy when the model was calibrated. As for the vine crop, the best fit was obtained for K_{cb full ini} = 0.33, K_{cb full mid} = 0.684, and K_{cb full end} = 0.54. In this sense, the irrigation schedule must adjust these coefficients to local conditions to achieve economically and environmentally sustainable production.

The influence of the colour of photo-selective nets on Actinidia deliciosa yield, fruit quality and progression of the bacterial kiwifruit canker (Pseudomonas syringae pv. actinidiae, Psa) need to be characterised due to increasing use of these nets, mainly to protect from hail and storms. From May 2019 onwards, pearl (Pn), yellow (Yn) and grey (Gn) nets were installed permanently in a ‘Hayward’ kiwifruit orchard in NW Portugal and uncovered plants were used as the control. Compared to outside conditions for both seasons, the blue:red ratio and the mean air temperature were higher (mean increase of 12.7% and 0.6 °C, respectively) and the photosynthetic active radiation (PAR) was lower (10.8% less between budbreak and bloom) under the Pn. Crop yield, compared to the control, decreased by 40.3% under the three nets in 2020, and by 23.9% under the Yn and Gn in 2021. Yield and fruit grade under the Pn were similar to that of uncovered crops in 2021, and fruit grade was overall higher under the Pn compared to the Yn and Gn. Photo-selective nets did not affect the fruit quality parameters. Psa progression decreased under the Pn compared to the control during two months in both seasons, although this beneficial impact needs further evaluation.

Context: Downy mildew, caused by Plasmopara viticola , remains one of the most damaging diseases affecting grapevines, especially in humid viticultural regions such as the “Vinhos Verdes” in northern Portugal. Traditional detection relies on visual inspection and laboratory techniques, which are subjective and reactive, often delaying effective intervention. Aims: This study aims to evaluate the potential of field spectroscopy combined with machine learning to detect downy mildew in Vitis vinifera cv. Loureiro field conditions. The focus is on providing an early, non-destructive detection method that can be used in precision viticulture, reducing the need for costly, widespread pesticide applications. Methods and Key Results: Leaf spectral reflectance data were collected along the 2023 and 2024 growing seasons using a portable spectroradiometer. Measurements were obtained from both untreated and fungicide-treated grapevines, covering different infection stages. Spectral signatures from 600 grapevine leaves were used to train and validate classification models using Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Random Forest (RF) models. Both RF and PLS-LDA models showed an overall accuracy of 95.1% when trained with all spectral features from the dataset. Red edge (700–750 nm) and visible (400–700 nm) wavelengths demonstrated the highest classification contribution. Moreover, the twenty most informative wavelengths for infection discrimination were identified for each model. Conclusion: The results confirm the effectiveness of field spectroscopy, making it possible to detect downy mildew symptoms in different stages of infection. This method offers a rapid, cost-effective, and sustainable tool for early disease detection, which can greatly benefit winegrowers by enabling more timely and specific interventions and reducing the reliance on chemical treatments. Implications and Impacts: This study demonstrates a novel approach to precision viticulture, offering winegrowers a rapid, cost-effective, and sustainable tool for early disease detection. The methodology not only promotes more disease management strategies but also aligns with environmental and regulatory goals.