Quantification of water stress and yield estimation in grapevine (Vitis Vinifera l.) using unmanned aerial vehicles (UAVS)
- Autores: Patricia López García
- Directores de la Tesis: José Fernando Ortega Álvarez (dir. tes.), Rocío Ballesteros González (codir. tes.)
- Lectura: En la Universidad de Castilla-La Mancha ( España ) en 2022
- Idioma: español
- Tribunal Calificador de la Tesis: Javier José Cancela Barrio (presid.), Rocío Arias-Calderón (secret.), Ramón López Urrea (voc.)
- Programa de doctorado: Programa de Doctorado en Ciencias Agrarias y Ambientales por la Universidad de Castilla-La Mancha
- Materias:
- Enlaces
- Tesis en acceso abierto en: RUIdeRA
- Resumen
Changes in climate caused by natural processes and, to a greater extent, by human activities, affect the availability of water resources and crop productivity, with agriculture being the activity with the highest water consumption. In this context of water limitation, precision irrigation in its different modalities is one of the techniques used by precision agriculture to increase the efficiency of water use, reducing its consumption and minimising the negative effects on the final productivity of crops. Deficit irrigation has been widely adopted in woody crops, with vines standing out for their importance at a global, national and regional level.
To implement any of the precision irrigation techniques, it is necessary to monitor the water status of the plant. Traditionally, point measurements have been used, which are not representative of the spatial variability of water status and require manpower and time for their execution, such as water potential, leaf transpiration and trunk diameter fluctuations, among others.
The use of satellite imagery by precision agriculture for over 40 years has contributed to the development of remote sensing-based monitoring and management techniques. The quick advancement of unmanned aerial vehicles (UAVs) or drones and sensors, and their adoption by precision agriculture has provided an unprecedented source of spatial, spectral and temporal resolution data, being widely used for crop monitoring and management, and for the assessment of vineyard water status as support to irrigation scheduling. The optimisation of irrigation water in the form of precision irrigation, as well as being important for saving water, constitutes a technique for regulating the final harvest in terms of quantity and quality. However, in order to apply it for this purpose, it is necessary to know the final production that can be expected at the time of harvest. Different methods based on the study of yield variables such as number of bunches per vine, bunch weight, number of berries per bunch or berry weight, and based on historical harvest data, have traditionally been used for this purpose. Images of inflorescences or bunches taken in the field with hand-held sensors, on board vehicles driving between rows of plants or in UAVs are the most recent developments in vineyard yield estimation.
The general objective of this Thesis is to analyse the relation of spectral and geometric parameters of the vineyard canopy derived from sensors on board UAVs to the water status and final yield measured in a vineyard, as a basis for the generation of predictive models of the water status and harvest at different times of vineyard development. For this purpose, from 2018 to 2020, different flights were carried out with a UAV with a conventional or RGB (red-green-blue) camera and a multispectral camera on board during the growth cycle of a trellised vineyard of the cv. Monastrell, located in Albacete (southeast Spain). The orthoimages were segmented excluding soil and shadows, thus obtaining only vegetation cover information from which the degree of vegetation cover (representative of vigour) and spectral bands were extracted and used to calculate different vegetation indices (VIs).
The experimental design consisted of four randomised blocks and six treatments, including rainfed and irrigated, as well as treatments combining different types of salt (sulphates and chlorides) with different irrigation start times (before and after veraison). Stem water potential was measured at midday on the same days that the UAV was flown, in order to calculate the water stress integral representative of the accumulated water stress. The final yield at harvest and the components of yield at the berry pea size stage were also measured.
Regarding the water status prediction, the VIs or spectral bands in the visible range were better related than the VIs derived from the multispectral sensor, with the multispectral bands showing more stable results when the interannual effect was analysed. In any case, field measurements are required every year to quantify the differences in water status, due to the interannual response of the crop to biotic and abiotic factors, and to the variability of soil, climatic and crop development conditions. Regarding the final yield estimation at early stages of bunch development, good relations to final yield have been found for visible and multispectral VIs combined with yield components measured in berry pea size and an indicator of vegetative vigour. The relations of VIs in the visible on berry pea size and closed bunch are outstanding. However, as in the case of water status prediction, it is necessary to calibrate these models annually due to inter-annual and intra-plot variability.
