Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.

Canopy characteristics are crucial for accurately and safely determining the pesticide quantity and volume of water used for spray applications in vineyards. The inevitably high degree of intraplot variability makes it difficult to develop a global solution for the optimal volume application rate. Here, the design procedure of, and the results obtained from, a variable rate application (VRA) sprayer are presented. Prescription maps were generated after detailed canopy characterization, using a multispectral camera embedded on an unmanned aerial vehicle, throughout the entire growing season in Torrelavit (Barcelona) in four vineyard plots of Chardonnay (2.35 ha), Merlot (2.97 ha), and Cabernet Sauvignonn (4.67 ha). The maps were obtained by merging multispectral images with information provided by DOSAVIÑA^®, a decision support system, to determine the optimal volume rate. They were then uploaded to the VRA prototype, obtaining actual variable application maps after the application processes were complete. The prototype had an adequate spray distribution quality, with coverage values in the range of 20–40% and exhibited similar results in terms of biological efficacy on powdery mildew compared to conventional (and constant) application volumes. The VRA results demonstrated an accurate and reasonable pesticide distribution, with potential for reduced disease damage even in cases with reduced amounts of plant protection products and water.

En este trabajo se presenta el desarrollo de un mapa de aplicación variable real obtenido con un prototipo de aplicación variable de productos fitosanitarios en base a mapas de vegetación obtenidos con un UAV. Los ensayos se realizaron en Torrelavit (Alt Penedès, Barcelona) en una parcela representativa de 3 ha de viña de la variedad Merlot plantada a 1,2 m de distancia entre plantas y a 2,2 m entre hileras. Los ensayos se llevaron a cabo durante el estadio vegetativo 75 según la escala BBCH (bayas de tamaño guisante). El mapa de prescripción se generó a partir de la unión entre la información tomada con una cámara multiespectral embarcada en un UAV y las recomendaciones obtenidas con la APP Dosaviña® (https://dosavina.upc.edu). Este mapa de prescripción se cargó en el pulverizador equipado con el sistema de aplicación variable.El mapa de vigor se generó usando un hexacopeto equipado con una cámara multiespectral con 5 bandas espectrales (R, G, B, NIR, RedEgde). A partir de las imágenes aéreas se calculó el índice NDVI y se establecieron tres niveles de vigor (alto, medio y bajo). Estas zonas de vigor fueron comprobadas mediante medidas manuales de la vegetación y se calculó el volumen de aplicación óptimo para cada una de ella utilizando la APP Dosaviña®El prototipo funciona de la siguiente manera: a) obtención de la posición GPS del pulverizador, b) determinación del volumen de aplicación objetivo a partir del mapa de prescripción considerando la posición GPS, c) lectura de la presión de pulverización real, d) reajuste de la presión de pulverización para la obtención del volumen objetivo, y e) almacenamiento de información cada segundo, almacenando información sobre la velocidad de avance real, la presión real de trabajo y el volumen de aplicación real. Este mapa de aplicación real permite obtener datos de trazabilidad en todas las zonas tratadas y calcular el ahorro de pesticidas en comparación con la aplicación convencional. Los resultados preliminares permitieron alcanzar un ahorro de alrededor del 20% tanto en la cantidad de producto fitosanitario como en el volumen de agua. Campos Tobajas, Javier; Gallart Palacios, Montserrat; Llop Casamada, Jordi; Salcedo Cidoncha, Ramón; Ortega Rioja, Paula; Armengol Esplugas, Enric; Gil Moya, Emilio