BUSCADOR RECOLECTA

The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping., This research was funded by the projects DECIMAL (PID2020-113229RB-C41), PRODIGIA (PID2020-113229RB-C44), AGVANCE (AGL2013-48297-C2-2-R), and PAgPROTECT (PID2021-126648OB-I00), from the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033). The authors also wish to thank Codorníu SA, celler Raimat and IRTA for having allowed the use of their vineyards, pear and peach orchards to conduct the trials described in this work.

Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions., This research was funded by the projects AGVANCE (AGL2013-48297-C2-2-R), PAgPROTECT (PID2021-126648OB-I00), DECIMAL (PID2020-113229RB-C41) and PRODIGIA (PID2020-113229RB-C44) from the Spanish Ministry of Science and Innovation MICIN/AEI/10.13039/501100011033/FEDER, UE. The authors also want to thank Codorníu SA and Celler Raimat for allowing the use of one of their vineyards to conduct the trial described in this work.

Hedgerow cultivation systems have revolutionized olive growing in recent years because of the mechanization of harvesting. Initially applied under irrigated conditions, its use has now extended to rainfed cultivation. However, there is limited information on the behavior of olive cultivars in hedgerow growing systems under rainfed conditions, which is a crucial issue in the context of climate change. To fill this knowledge gap, a rainfed cultivar trial was planted in 2020 in Southern Spain to compare ‘Arbequina’, ‘Arbosana’, ‘Koroneiki’, and ‘Sikitita’, under such growing conditions. One of the most important traits in low-water environments is the canopy growth. Because traditional canopy measurements are costly in terms of time and effort, the use of light detection and ranging (LiDAR) sensor onboard an uncrewed aerial vehicle (UAV) was tested. Statistical analyses of data collected in November 2022 and January 2023 revealed high correlations between UAV-LiDAR metrics and field measurements for height, projected area, and crown volume, based on validation with measurements from 36 trees. These results provide a solid basis for future research and practical applications in rainfed olive growing, while highlighting the potential of UAV-LiDAR technology to characterize tree canopy structure efficiently.

El crecimiento vegetativo de un viñedo influye en su vigor y su potencial productivo, y está determinado por parámetros como la superficie foliar externa (SA). Antes de la aplicación de las técnicas de teledetección en agricultura, la estimación de la SA se realizaba mediante laboriosos trabajos de campo que requerían planificación y consumo de recursos humanos y tiempo. Para solventar este problema, presentamos una metodología basada en imágenes-UAV y fotogrametría capaz de estimar la SA de cada cepa y cartografiar su variabilidad espacial. Estos resultados permiten avanzar en Viticultura de Precisión (VP) y la digitalización del viñedo.

Hedgerow orchard is an olive growing system where trees are planted at a super high-density higher than 20-fold (i.e., 1200–2500 trees ha−1) compared to the traditional density of olive orchards (usually 50 to 160 trees ha−1). It is dominating a great proportion of new plantations because harvesting can be fully mechanized, it is early bearing and has a relatively constant high productivity. However, there are a limited number of cultivars with sufficiently low vigour to be suitable for such plantation densities. For that reason, a set of low vigour cultivars and breeding selections has been used in a field experiment as rootstocks for reducing the vigour of “Picual”, the most frequent cultivar planted in Spain. Tree vigour was characterized by measuring crown height, projected and side areas, and volume through the analysis of photogrammetric point clouds created from images acquired with an unmanned aerial vehicle. A significant reduction of the ‘Picual’ vigour was observed in most of the rootstocks tested, with canopy volume reduced up to one half. High variability on vigour, first harvesting and their relative relationship was observed between the different rootstocks used. This indicates there might be enough genetic variability to perform breeding selection for dwarfing rootstocks on ‘Picual’ olive cultivar., This research was financed by the PID2020-113229RB-C44 (Spanish Ministry of Science & Innovation-ERDF: European Regional Development Fund), AVA2016.01.2 and AVA2019.027 partially funded by ERDF, and Intramural-CSIC 202040E230 projects. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

[ES] Uno de los elementos cruciales de la viticultura de precisión es conocer la variabilidad espacial del crecimiento vegetativo del viñedo para caracterizar su vigor y estimar su potencial productivo. Dos de los parámetros relacionados con este crecimiento son, la superficie foliar externa (SA) y el peso de la madera de poda, cuya estimación en campo requiere laboriosos trabajos que implican consumo de recursos humanos y tiempo. La utilización de técnicas de teledetección basadas en aplicación de técnicas fotogramétricas en imágenes adquiridas mediante vehículo aéreo no tripulado (UAV) ha demostrado su eficiencia en la cartografía de la arquitectura de cultivos leñosos como viñedo, almendro u olivo. Por ello, el objetivo del presente trabajo fue desarrollar una metodología capaz de determinar de forma precisa, por un lado, la SA y, por otro, estudiar la relación entre madera de poda y el volumen en viñedos de la variedad 'Pedro Ximénez' manejados mediante cultivo en sistema ecológico y conducido en espaldera. El procedimiento desarrollado está basado en la generación y procesamiento de nubes de puntos fotogramétricas en cada cepa que son posteriormente analizadas utilizando un algoritmo completamente automatizado de análisis de imagen basado en objetos (OBIA, object‑based‑image‑analysis). Los resultados obtenidos por métodos directos no destructivos tomados en campo fueron comparados con los generados mediante imágenes‑UAV. Se obtuvieron correlaciones significativas entre los datos observados y los estimados indicando la utilidad de la metodología descrita para avanzar en la caracterización foliar de cada cepa y la digitalización del viñedo a escala parcela reduciendo las mediciones de campo., [EN] One of the crucial elements for precision viticulture and site-specific management is to assess the spatial variability of vegetative growth for an accurate characterization of vigor and further estimation of yield forecast. Two of the main parameters related to vegetative growth are External Leaf Area (SA) and weight of pruning wood, and both have been traditionally estimated by using methods rely on manual sampling. These methods are time-consuming making it difficult to handle the intrinsic spatial variability of vineyards. The application of remote sensing based on photogrammetric techniques and OBIA (object-based-image-analysis) to images acquired with an Unmanned Aerial Vehicle (UAV) has shown to be an efficient way to derive accurate three-dimensional (3D) canopy information in woody crops such as vineyard, olive or almond. In this context, a set of dense 3D point clouds of every vine was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an UAV in two vineyards with ‘Pedro Ximénez’ variety drip-irrigated, trellis-trained and managed under organic system. Point clouds were then analyzed by using an OBIA automatic algorithm to accurately assess SA and to study the relationship between weight of pruning wood and vine volume. Results from a nondestructive field sampling and estimated by UAV-imagery were compared. Significant correlations between observed and estimated data were recorded indicating the utility of the procedure developed for an accurate characterization of every vine vegetative growth. This opens the door to progress in digitizing applications in vineyards., Esta investigación fue financiada por los proyectos PID2020-113229RB-C44 (Mº de Ciencia e Innovación y Fondos FEDER), INTRAMURAL-CSIC 202040E230 y TRANSVITI (Proyecto de Transferencia y Cooperación en Vitivinicultura Andaluza, ref.: PP.TRA.TRA2019.007, IFAPA, cofinanciado Fondos FEDER, Programa
Operativo FEDER-Andalucía 2014-2020)., Peer reviewed

10 Pág., The measurement of geometric canopy parameters in woody crops is an important task in Precision Agriculture because of their correlation with crop condition and productivity. In recent years, several technological approaches have been developed as an alternative to manual measurements, which are time- and labour-consuming. Two of the most commonly used 3D canopy characterization technologies are mobile terrestrial laser scanning (MTLS) based on light detection and ranging (LiDAR) sensors, and digital aerial photogrammetry (DAP) using imagery from uncrewed aerial vehicles (UAVs). Although both are state-of-the-art and have been fully tested and validated, a complete comparison between their geometric canopy parameter estimations in different woody crops and training systems has not been carried out. For this reason, a set of geometric parameters (canopy height, projected area, and volume) of a vineyard, an intensive peach orchard, and an intensive pear orchard were measured using UAV-DAP and MTLS-LiDAR. A comparison between both kinds of measurements was performed, accounting for the length of the sections in which the crop hedgerows were divided to extract the geometric parameters. Measurements from the UAV and the MTLS were highly correlated (R2 from 0.82 to 0.94) when considering the data from the three crops together, and the correlations were higher when analysing longer row sections. The canopy geometric parameters estimated using the MTLS-LiDAR always had higher values than those from the UAV-DAP. The results presented in this work provide useful data for a more informed selection of technological approaches for 3D crop characterization in Precision Fruticulture and high-throughput phenotyping., This research was funded by the projects DECIMAL (PID2020-113229RB-C41), PRODIGIA (PID2020-113229RB-C44), AGVANCE (AGL2013-48297-C2-2-R), and PAgPROTECT (PID2021-126648OB-I00), from the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033). The authors also wish to thank Codorníu SA, celler Raimat and IRTA for having allowed the use of their vineyards, pear and peach orchards to conduct the trials described in this work., Peer reviewed

Poppy (also common poppy or corn poppy; Papaver rhoeas L., PAPRH) is one of the most harmful weeds in winter cereals. Knowing the precise and accurate location of weeds is essential for developing effective site-specific weed management (SSWM) for optimized herbicide use. Among the available tools for weed mapping, deep learning (DL) is used for its accuracy and ability to work in complex scenarios. Crops represent intricate situations for weed detection, as crop residues, occlusion of weeds, or spectral similarities between crop and weed seedlings are frequent. Timely discrimination of weeds is needed, because postemergence herbicides are used just when weeds and crops are at an early growth stage. This study addressed P. rhoeas early detection in wheat (Triticum spp.) by comparing the performance of six DL-based object-detection models focused on the You Only Look Once (YOLO) architecture (v3 to v5) using proximal RGB images to train the models. The models were assessed using open-source software, and evaluation offered a range of results for quality of recognition of P. rhoeas as well as computational capacity during the inference process. Of all the models, YOLOv5s performed best in the testing phase (75.3%, 76.2%, and 77% for F1-score, mean average precision, and accuracy, respectively). These results indicated that under real field conditions, DL-based object-detection strategies can identify P. rhoeas at an early stage, providing accurate information for developing SSWM., This research was financed by project PID2020-113229RB-C44 (AEI/10.13039/501100011033) (Spanish MCIN funds)., Peer reviewed

© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)., Characterizing crop canopies is especially important in the management of woody crops. In this article, two systems were compared to characterise a 50 m long vineyard row section. One of the systems was a mobile terrestrial laser scanner based on a light detection and ranging (LiDAR) sensor (MTLS-LiDAR). The other was an uncrewed aerial vehicle (UAV) based system using digital aerial photogrammetry (UAV-DAP). The resulting 3D point clouds were assessed qualitatively and quantitatively. Canopy heights, widths and volumes were obtained in 0.1 m long sections along the studied row. All the parameters derived from the two systems presented statistically significant differences. The coefficients of determination between systems were 0.619 for canopy maximum heights above ground level (agl), 0.686 for 90th percentile (P90) heights agl, and 0.283 and 0.274 for maximum and P90 vegetated heights, respectively. Coefficients of determination between averaged maximum canopy width and P90 canopy width were 0.328 and 0.317, respectively. Coefficients of determination between cross-sectional areas determined from maximum widths, P90 widths and from the occupancy grid method were 0.423, 0.409 and 0.334, respectively. Total canopy volume for the entire row obtained from the three cross section estimation methods differed between 19 m3 and 25 m3. The reasons found were that the MTLS-LiDAR-derived point cloud captured the canopy top and side variability but could be affected by occlusions, mixed pixels and tall grass-like weeds present in the surveyed area. For its part, the UAV-DAP-derived point cloud tended to miss top and side shoots and somewhat smoothed canopy variability. As neither of the systems is optimal, a balance needs to be found according to the specific requirements of the survey. For this purpose, a list of pros and cons is presented to support the selection of one of the two systems for canopy monitoring. The MTLS-LiDAR system should be chosen when high detail is required but small areas are to be scanned. Alternatively, the UAV-DAP system should be chosen when large areas are to be monitored and when canopy detail is not so important. Further results are presented in Part 2 for a larger area and including pear and peach orchards with different training systems. Future research is to be conducted on how the compared systems affect variability detection and support variable-rate prescriptions., This research was funded by the projects AGVANCE (AGL2013-48297-C2-2-R), PAgPROTECT (PID2021-126648OB-I00), DECIMAL (PID2020-113229RB-C41) and PRODIGIA (PID2020-113229RB-C44) from the Spanish Ministry of Science and Innovation MICIN/AEI/10.13039/501100011033/FEDER, UE., Peer reviewed

The use of cover crops in vineyards is expected to increase due to the strong encouragement by European agricultural policy and their contribution to reducing soil erosion. This paper presents the results obtained over three years in a vineyard of the “Pedro Ximénez” variety organically grown in southern Spain. The influence on production, vigor, and grape quality of a seeded cover crop versus tillage was compared using field data and imagery acquired by an uncrewed aerial vehicle. The vines under tillage showed greater vegetative development and yield than those with cover crops between rows. The grapes from the vines under the cover crop treatment ripened earlier and presented higher values of total soluble solids, characteristics that can be useful in the protected designation of origin where the study field is placed. However, the strong yield reduction caused by the cover crop treatment encourages future research to explore other cover crop species that could contribute to improving soil properties without compromising the profitability of the vineyard. This is the first time that the influence of cover cropping on the agronomic and oenological parameters of organically grown white vineyard varieties such as “Pedro Ximénez” has been assessed using field and UAV data., Funding for open access publishing: Universidad de Córdoba/CBUA. This research was funded by projects PID2020-113229RB-C44 (MCIN/AEI/https://doi.org/10.13039/501100011033) and TRANSVITI (Transferencia y Cooperación en Vitivinicultura Andaluza, PP.TRA.TRA2019.007, Programa Operativo FEDER-Andalucía 2014-2022)., Peer reviewed

Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter-row area infesting the natural or sown cover crops, and is considered a hard to control weed. Research in other woody crops has shown E. elaterium distribution in stands or patches and weed-free areas, which makes this weed susceptible to design a site-specific control strategy only addressed to E. elaterium patches. Therefore, our work adresses to develop a methodology based on the analysis of imagery acquired with an unmanned aerial vehicle (UAV) to detect and map E. elaterium infestations in hedgerow olive orchards for further design of site-specific control maps only for the E. elaterium infested areas.

[Description of methods used for collection/generation of data] UAV flights and field work were conducted on 05/19/2021 and on 09/27/2021. Coordinates (XYZ) of the E. elaterium plants and patches were registered using a RTK GNSS. Aerial images were acquired with a quadcopter model Mavic Pro 2 (DJI, Shenzen, China) equipped with an RGB camera Hasselblad LID-20c with 20 Mp. The UAV was configured for flights at 50 m over the terrain following flight lines parallel to the olive trees lines. The aerial images had forward and side overlaps of 90% and 85%, respectively; UAV flights were carried around noon on sunny days with no wind.

UAV images were processed with Agisoft Metashape Professional Edition software (Agisoft LLC, St. Petersburg, Russia) for the generation of the geomatic products used in the classification workflows: 1) the orthomosaic containing the spectral information, and 2) the Digital Surface Model (DSM) which provided height information. The orthomosaics and the DSMs from both fields and dates had a spatial resolution of 1.1 cm and 2.2 cm, respectively. The production of the geomatic products was automatic, with the exception of the manual measurement of 5 ground control points (GCPs) in the images (one in each corner plus one in the field center). GCP coordinates were measured in the field with a RTK GNSS receiver the day of flights., Ecballium elaterium (common name: squirting cucumber) is an emerging weed problem in hedgerow or superintensive olive groves under no tillage. It colonizes the inter-row area infesting the natural or sown cover crops, and is considered a hard-to-control weed. Research in other woody crops has shown E. elaterium has a patchy distribution, which makes this weed susceptible to design a site-specific control strategy only addressed to E. elaterium patches which would allow the design of efficient and minimal herbicide control protocol and a step forward in sustainable weed management. Therefore, the aim of this work was to develop a methodology based on the analysis of imagery acquired with an uncrewed aerial vehicle (UAV)
to detect and map E. elaterium infestations in hedgerow olive orchards., UAV flights and field work were conducted on 05/19/2021 and on 09/27/2021. Coordinates (XYZ) of the E. elaterium plants and patches were registered using a RTK GNSS. Aerial images were acquired with a quadcopter model Mavic Pro 2 (DJI, Shenzen, China) equipped with an RGB camera Hasselblad LID-20c with 20 Mp. The UAV was configured for flights at 50 m over the terrain following flight lines parallel to the olive trees lines. The aerial images had forward and side overlaps of 90% and 85%, respectively. UAV flights were carried around noon on sunny days with no wind., UAV images were processed with Agisoft Metashape Professional Edition software (Agisoft LLC, St. Petersburg, Russia) for the generation of the geomatic products used in the classification workflows: 1) the Digital Surface Model (DSM) which provided height information, and 2) orthomosaic containing the spectral information. The orthomosaics and the DSMs from both fields and dates had a spatial resolution of 1.1 cm and 2.2 cm, respectively. The production of the geomatic products was automatic, with the exception of
the manual measurement of 5 ground control points (GCPs) in the images (one in each corner plus one in the field center). GCP coordinates were measured in the field with a RTK GNSS receiver the day of flights., This research was funded by MCIN/AEI/10.13039/501100011033 (project:
PID2020-113229RB-C44; acronym: PRODIGIA. Title: Optimimizing crop PROductivity for DIGItazing applications: integration of Architecture and spectral information., i) Ecballium Field1 Digital Surface Model May: geotiff file "" ii) Ecballium Field 1 Orthomosaic May: geotiff file "" iii) Ecballium Field 2 Digital Surface Model May: geotiff file "" iv) Ecballium Field 2 Orthomosaic May: geotiff file "" v) Ecballium Field 1 Digital Surface Model September: geotiff file "" vi) Ecballium Field 1 Orthomosaic September : geotiff file "" vii) Ecballium Field 2 Digital Surface Model September: geotiff file "" viii) Ecballium Field 2 Orthomosaic September: geotiff file "" ix) Ecballium patches coordinatesXYZ May: csv file "", Peer reviewed

This paper studies the regional consumption of household water in Spain in the period 2000–2018. The use of the methodology proposed by Phillips and Sul allows us to conclude that there is no single pattern of behavior across the Spanish regions. By contrast, we can determine the existence of three convergence clubs, confirming serious regional disparities in water consumption. Navarra, País Vasco, La Rioja, and Cataluña are included in the convergence club that shows the lowest levels of household water consumption, while the Islas Canarias, Comunidad Valenciana, Castilla y León and Cantabria belong to that with the highest consumption. The determinants of the forces that drive these convergence clubs are difficult to identify because the demographic, economic and structural variables of the network interact in different ways. Nevertheless, we can select a group of explanatory variables that help to explain the formation of the convergence clubs. These are regional household income, the birth rate in the regions, and the regional spending on environmental protection. Increments in the levels of these variables are helpful for reducing household water consumption. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

This paper studies the existence of international health outcome disparities. We focus on the use of the potential years of life lost for a database that includes information from 33 OECD countries and covers the period 1990–2017. The methodology proposed by Phillips and Sul (2007) allows us to reject the existence of a single pattern of behaviour between countries for both males and females, suggesting the existence of severe health outcome inequalities. This methodology estimates the existence of four convergence clubs whose composition slightly varies when comparing the male and female cases. Some socioeconomic factors are found to be very important in explaining the forces that may drive the creation of these convergence clubs. In particular, the evolution of the economy and health policies are pivotal to understanding the creation of these estimated convergence clubs. Additionally, our results offer evidence in favor of the importance of environmental policies to explain these health outcome differences.