https://hdl.handle.net/10259/7491 2026-05-06T10:54:59Z https://hdl.handle.net/10259/10062 Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery Vélez Martín, Sergio; Poblete Echeverría, Carlos; Rubio Cano, José Antonio; Vacas Izquierdo, Rubén; Barajas, Enrique A few decades ago, farmers could precisely monitor their croplands just by walking over the fields, but this task becomes more difficult as farm size increases. Precision viticulture can help better understand the vineyard and measure some key structural parameters, such as the Leaf Area Index (LAI). Remote Sensing is a typical approach to monitoring vegetation which measures the spectral information directly emitted and reflected from vegetation. This study explores a new method for estimating LAI which measures the projected shadows of plants using UAV (unmanned aerial vehicle) imagery. A flight mission over a vineyard was scheduled in the afternoon (15:30 to 16:00 solar time), which is the optimal time for the projection of vine shadows on the ground. Real LAI was measured destructively by removing all the vegetation from the area. Then, the projected shadows in the image were detected using machine learning methods (k-means and random forest) and analysed at pixel level using a customised R code. A strong linear relationship (R² = 0.76, RMSE = 0.160 m² m-2 and MAE = 0.139 m² m-2) was found between the shaded area and the LAI per vine. This is a quick and simple method, which is non-destructive and gives accurate results; moreover, flights can be scheduled during other periods of the day than solar noon, such as in the morning or afternoon, thus enabling pilots to extend their working day. Therefore, it may be a viable option for determining LAI in vineyards trained on Vertical Shoot Positioned (VSP) systems. 2021-11-01T00:00:00Z https://hdl.handle.net/10259/10061 Agronomic classification between vineyards ('Verdejo') using NDVI and Sentinel-2 and evaluation of their wines Vélez Martín, Sergio; Rubio Cano, José Antonio; Andrés, María Isabel; Barajas, Enrique A classification between three vineyards belonging to the Appellation of Origin Rueda (Castilla y León, Spain) has been established in veraison to determine the productive capacities of each vineyard and to study their impact on the grape quality. Several open-access multispectral images obtained from the SENTINEL-2A satellite in the year 2016 were used to calculate the NDVI (Normalized Difference Vegetation Index), which provides information about the vigour of the vineyards. Eleven cloud-free images were assessed and based on the NDVI, three vigour levels were established: high vigour (0.356-0.458), medium vigour (0.285-0.355) and low vigour (0.166-0.284). A level of vigour was assigned to each vineyard according to the NDVI mean values of its pixels. Significant differences were found in the pruning wood weight and yield: high, medium and low vigour values were 2438, 1895 and 1487 kg·ha-1 and 15984, 12990 and 10576 kg·ha-1, respectively. The highest values of total acidity (6.04 g·L-1) and tartaric acid (9.05 g·L-1) have been obtained in low vigour, as well as the lowest values of pH (3.26), malic acid (0.42 g·L-1) and potassium (1640 ppm). Finally, one wine per vigour was produced and a tasting was carried out to check if the differences between the vineyards were perceptible. According to the obtained results, the NDVI is a good indicator to classify vineyards, finding notable differences between the experimental treatments studied. 2019-01-01T00:00:00Z https://hdl.handle.net/10259/10060 Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits Ariza Sentís, Mar; Baja, Hilmy; Vélez Martín, Sergio; Valente, João Grapevine phenotyping is the process of determining the physical properties (e.g., size, shape, and number) of grape bunches and berries. Grapevine phenotyping information provides valuable characteristics to monitor the sanitary status of the vine. Knowing the number and dimensions of bunches and berries at an early stage of development provides relevant information to the winegrowers about the yield to be harvested. However, the process of counting and measuring is usually done manually, which is laborious and time-consuming. Previous studies have attempted to implement bunch detection on red bunches in vineyards with leaf removal and surveys have been done using ground vehicles and handled cameras. However, Unmanned Aerial Vehicles (UAV) mounted with RGB cameras, along with computer vision techniques offer a cheap, robust, and timesaving alternative. Therefore, Multi-object tracking and segmentation (MOTS) is utilized in this study to determine the traits of individual white grape bunches and berries from RGB videos obtained from a UAV acquired over a commercial vineyard with a high density of leaves. To achieve this goal two datasets with labelled images and phenotyping measurements were created and made available in a public repository. PointTrack algorithm was used for detecting and tracking the grape bunches, and two instance segmentation algorithms - YOLACT and Spatial Embeddings - have been compared for finding the most suitable approach to detect berries. It was found that the detection performs adequately for cluster detection with a MODSA of 93.85. For tracking, the results were not sufficient when trained with 679 frames.This study provides an automated pipeline for the extraction of several grape phenotyping traits described by the International Organization of Vine and Wine (OIV) descriptors. The selected OIV descriptors are the bunch length, width, and shape (codes 202, 203, and 208, respectively) and the berry length, width, and shape (codes 220, 221, and 223, respectively). Lastly, the comparison regarding the number of detected berries per bunch indicated that Spatial Embeddings assessed berry counting more accurately (79.5%) than YOLACT (44.6%). 2023-08-01T00:00:00Z https://hdl.handle.net/10259/10059 A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume Vélez Martín, Sergio; Vacas Izquierdo, Rubén; Martín Gutiérrez, Hugo; Ruano Rosa, David; Álvarez Martín, Sara Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R2 > 0.8 (pixel-based classification) and R2 > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops. 2022-11-01T00:00:00Z