BUSCADOR RECOLECTA

[ES] Uno de los mayores retos en la agricultura de precisión es la discriminación entre malas hierbas y cultivos en las imágenes digitales obtenidas por UAVs (vehículos aéreos no tripulados), ya que la similitud espectral entre ambos puede llevar a una clasificación errónea. Varios autores han propuesto técnicas para llevar a cabo esta tarea, que se debe realizar con un software comercial especializado, lo que requiere un alto conocimiento previo sobre computación y modelado, y es costoso. Por todo ello, el objetivo de este trabajo fue desarrollar una metodología para detectar y mapear áreas ocupadas por la mala hierba Cynodon dactylon (grama) en viñedos, utilizando imágenes aéreas digitales de alta resolución obtenidas por vehículos aéreos no tripulados (UAVs) y herramientas de acceso libre. C. dactylon es una gramínea perenne de verano que compite con el viñedo por los nutrientes y el agua, y se propaga principalmente por reproducción vegetativa mediante estolones y fragmentación de rizomas, lo que dificulta un control adecuado. Esta investigación se llevó a cabo en dos viñedos orgánicos de riego por goteo experimentales, ubicados en Cabra (provincia de Córdoba, sur de España). Las imágenes se capturaron con dos cámaras: RGB (rango espectral: rojo, verde, azul) y RG + infrarrojo rojo (NIR). Las cámaras se acoplaron por separado en un UAV modelo MD4-1000. El preprocesamiento de las imágenes recopiladas con cada cámara se realizó utilizando el software PhotoScan para obtener imágenes ortomosaicas precisas que cubran todos los campos para el análisis de clasificación. Las imágenes georreferenciadas resultantes se analizaron utilizando el software QGIS para identificar y clasificar las clases en el espacio entre hileras, suelo y vegetación (que estaba compuesta por áreas ocupadas por grama). Primero, se realizó un análisis espectral para determinar el índice de vegetación (VI) más efectivo para cada cámara. Los VI se seleccionaron realizando análisis estadísticos para cuantificar la separación del histograma, lo que proporcionó suficientes diferencias espectrales entre ambas clases (suelo y vegetación). ExG y GNDVI fueron los VI seleccionados para los sensores de rango RGB y RGNIR, respectivamente. Luego, como paso previo para la clasificación, se implementó la segmentación del área en ORFEO toolbox y la discriminación de las diferentes clases en la imagen se realizó mediante un proceso de umbralización de los atributos extraídos. La validación de los resultados se realizó mediante la generación de una matriz de confusión y el cálculo de la Precisión general y el Índice Kappa. Con respecto a la discriminación de malas hierbas, los resultados mostraron una precisión que oscilaba entre el 88 y 97% y un índice Kappa entre 0,59 y 0,93, respectivamente, para cada sensor, lo que indica una precisión considerada de "Moderada" (0,59) a "Casi perfecta" (0,93). Por lo tanto, se puede concluir que se obtuvieron resultados satisfactorios para la clasificación mediante los dos sensores utilizados, lo que demuestra la solidez de esta metodología para la clasificación de imágenes UAV de resolución espacial ultra alta. Las herramientas de código abierto utilizadas en este trabajo pueden aumentar la flexibilidad y la transferibilidad de la tecnología de procesamiento de imágenes. Además, los mapas generados de grama podrían ayudar a los agricultores a mejorar el control de malas hierbas a través de una estrategia bien programada basada en el manejo sitio-específico de malas hierbas., [EN] A major challenge in precision agriculture refers to the discrimination of weeds from crops in digital images obtained by Unmanned Aerial Vehicles (UAVs), since the spectral similarity between weed and crop plants can lead to misclassification. Several authors have proposed techniques to accomplish the task that must be performed by specialized commercial software, thus requiring high previous knowledge about computation and modelling, and also are expensive. Therefore, the objective of this work was to develop a methodology to detect and map Cynodon dactylon (bermudagrass) weed patches in vineyards using high resolution digital aerial images from an UAV and free access tools. C. dactylon is a perennial summer grass that competes with vineyard for nutrients and water, and it propagates mainly vegetatively by stolons and rhizome fragmentation, which makes difficult an appropriate control. This research was conducted in two experimental drip-irrigated organic vineyards, located in Cabra (Córdoba province, Southern Spain). The imagery were captured using two cameras: RGB (spectral range: Red, Green, Blue) and RG+Infrared Red (NIR). They were separately accoupled in a UAV model MD4-1000. The pre-processing of the images collected from each camera was performed using the PhotoScan software in order to obtain an accurate orthomosaicked imagery covering the whole fields for the further classification analysis. The resulting georeferenced images were analysed using QGIS software to indentify and classify the classes in the interrow space, this is soil and vegetation (which was composed by bermudagrass patches). First, spectral analysis to determine the most effective vegetation index (VI) for each camera was performed. The VIs were selected by performing statistical analysis to quantify the histogram separation, which provided sufficient spectral differences between both classes (soil and vegetation). ExG and GNDVI were the VIs selected for the RGB- and RGNIR-range sensors, respectively. Then, as a previous step for classification, segmentation of the area was implemented in ORFEO toolbox and the discrimination of the different classes in the image was performed by a thresholding process of the extract attributes. The validation of the results was performed through the generation of a confusion matrix, and the calculation of the Overall Accuracy and Kappa Index. Regarding the weed discrimination, the results showed an accuracy ranging from 88 to 97% and Kappa index ranging from 0.59 to 0.93, respectively for each sensor which shows accurate considered from "Moderate" (0.59) to "Almost Perfect" (0.93). Thus, it can be concluded that satisfactory results were obtained for the classification for the both sensors used, demonstrating the robustness of this methodology for the classification of ultra-high spatial resolution UAV-images. The open source tools used in this work can increase the flexibility and transferability of image processing technology. In addition, the generated bermudagrass maps could help farmers to improve weed control through a well-programmed strategy based on site-specific weed management., This research work has been developed as a result of a mobility stay funded by the Erasmus+ -KA1 Erasmus Mundus Joint Master Degrees Programme of the European Commission under the PLANT HEALTH Project. This research was funded by the AGL2017-82335-C4-4R project (Spanish Ministry of Science, Innovation and Universities, FEDER Funds: Fondo Europeo de Desarrollo Regional).

Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use ofUAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: (1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and (2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold., This research was partly financed by the AGL2017-83325-C4-4-R (Spanish Ministry of Science and Innovation AEI/EU-FEDER funds), DECIVID and VINO ROSADO (funds from the Government of Navarra, grant nos. 0011-1365-2017-000113 and 0011-1365-2019-000111), and Intramural-CSIC (grant nos. 201840E002 and 202040E230) projects. Research of Dr. de Castro was supported by the Juan de la Cierva-Incorporación Program. Diana Marin is beneficiary of a postgraduate scholarships funded by the Universidad Pública de Navarra (FPI-UPNA-2016), and Oihane Oneka of a Youth Guarantee grant for R+D (Ministry of Science and Universities, 17/5/2018). The authors acknowledge the support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

The potential loss of world crop production from the effect of pests,
including weeds, animal pests, pathogens and viruses has been quantifed as around
40%. In addition to the economic threat, plant diseases could have disastrous consequences for the environment. Accurate and timely disease detection requires the use
of rapid and reliable techniques capable of identifying infected plants and providing the tools required to implement precision agriculture strategies. The combination of
suitable remote sensing (RS) data and advanced analysis algorithms makes it possible to develop prescription maps for precision disease control. This chapter shows
some case studies on the use of remote sensing technology in some of the world’s
major crops; namely cotton, avocado and grapevines. In these case studies, RS has
been applied to detect disease caused by fungi using different acquisition platforms
at different scales, such as leaf-level hyperspectral data and canopy-level remote
imagery taken from satellites, manned airplanes or helicopter, and UAVs. The
results proved that remote sensing is useful, effcient and effective for identifying
cotton root rot zones in cotton felds, laurel wilt-infested avocado trees and escaaffected vines, which would allow farmers to optimize inputs and feld operations,
resulting in reduced yield losses and increased profts., The research presented here was partly financed by the USDA Specialty
Block Grant No. 019730 (Florida Department of Agriculture and Consumer Services, USA),
AGL2017-83325-C4-1R and AGL2017-83325-C4-4R Projects (Spanish Ministry of Science,
Innovation and Universities and AEI/EU-FEDER funds), Public University of Navarre postgraduate scholarships (FPI-UPNA-2017, Res.654/2017), Project DECIVID (Res.104E/2017,
Department of Economic Development of the Navarre Government-Spain), and the Spanish
MINECO project TIN2016-77356-P (AEI, Feder/UE).

[Background] Almond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates., [Results] Our method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield., [Conclusions] Our findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders., This research was funded by the AGL2017‑83325‑C4‑4R (Spanish Ministry of Science, Innovation and Universities, EU‑FEDER Funds), Transforma (ref.: PP.TRA.TRA.2016.00.6; PP.TRA.TRA.2019.00.2), UAVarAL (Ref. 1264556, Programa Operativo FEDER 2014‑2020 and Consejería de Economía y Conocimiento de la Junta de Andalucía) and INTRAMURAL 201840E002 (CSIC funds) projects., We acknowledge support of the publication fee by the CSIC Open Access Support Initiative through its Unit of Information Resources for Research (URICI), Peer reviewed

The establishment and management of cover crops are common practices widely used in irrigated viticulture around the world, as they bring great benefits not only to protect and improve the soil, but also to control vine vigor and improve the yield quality, among others. However, these benefits are often reduced when cover crops are infested by Cynodon dactylon (bermudagrass), which impacts crop production due to its competition for water and nutrients and causes important economic losses for the winegrowers. Therefore, the discrimination of Cynodon dactylon in cover crops would enable site-specific control to be applied and thus drastically mitigate damage to the vineyard. In this context, this research proposes a novel, automatic and robust image analysis algorithm for the quick and accurate mapping of Cynodon dactylon growing in vineyard cover crops. The algorithm was developed using aerial images taken with an Unmanned Aerial Vehicle (UAV) and combined decision tree (DT) and object-based image analysis (OBIA) approaches. The relevance of this work consisted in dealing with the constraint caused by the spectral similarity of these complex scenarios formed by vines, cover crops, Cynodon dactylon, and bare soil. The incorporation of height information from the Digital Surface Model and several features selected by machine learning tools in the DT-OBIA algorithm solved this spectral similarity limitation and allowed the precise design of Cynodon dactylon maps. Another contribution of this work is the short time needed to apply the full process from UAV flights to image analysis, which can enable useful maps to be created on demand (within two days of the farmer´s request) and is thus timely for controlling Cynodon dactylon in the herbicide application window. Therefore, this combination of UAV imagery and a DT-OBIA algorithm would allow winegrowers to apply site-specific control of Cynodon dactylon and maintain cover crop-based management systems and their consequent benefits in the vineyards, and also comply with the European legal framework for the sustainable use of agricultural inputs and implementation of integrated crop management., This research was partly financed by the AGL2017-83325-C4-4R, AGL2017-83325-C4-2R, AGL2017-83325-C4-1R (Spanish Ministry of Science, Innovation and Universities and AEI/EU-FEDER funds) and the Intramural-CSIC projects (ref. 201840E002). Research of de Castro and F. Valencia-Gredilla were supported by the Juan de la Cierva-Incorporación Program and University of Lleida, respectively., Peer reviewed

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of “linking genotype and phenotype,” considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders., The breeding field trials in which the experiments were performed are funded by Interaceituna (Spanish Inter-professional Association for Table Olives) through the FIUS projects PR201402347 and PRJ201703174. This research was partly financed by the AGL2017-83325-C4-4-R (Spanish Ministry of Science, Innovation and Universities and AEI/EU-FEDER funds), and Intramural-CSIC 201940E074 Projects. Research of AC was supported by the Juan de la Cierva Program-Incorporación of the Spanish MINECO funds., Peer reviewed

Accurately mapping the spatial distribution of weeds within a field is a first step towards effective Site-specific Weed Management. The main objective of this study was to investigate if the multivariate geostatistical method of cokriging (COK) can be used to improve the accuracy of Papaver rhoeas L. infestations maps in winter wheat fields using high-resolution UAV imagery as ancillary information. The primary variable was obtained by intensive grid weed density field samplings and the secondary variables were derived from the UAV imagery taken the same day as the weed field samplings (e.g. wavebands and derivative products, such as band ratios and vegetation indexes). Univariate Ordinary Kriging (OK) and multivariate cokriging (COK) interpolation methods were used and compared for Papaver density mapping. The performances of the different methods were assessed by cross-validation. The results indicated that COK outperformed OK in the spatial interpolation of Papaver density. COK reduced the prediction errors and enhanced the accuracy of Papaver estimates maps. The best performances were obtained when COK was performed with the UAV-secondary variables that yielded the highest correlation with Papaver density and produced the strongest spatial cross-semivariograms. On average, the COK with UAV-derived ancillary variables improved the accuracy of mapping Papaver density by 11 to 21% compared with OK. The results suggest the great potential of high-resolution UAV imagery as a source of ancillary information to improve the accuracy of spatial mapping of sparsely sampled target variables using COK., This research was financed by the AGL2014-52465-C4-4-R and AGL2017-83325-C4-4-R MINECO (Spanish Ministry of Economy and Competition, FEDER Funds). Research of AI. de Castro was financed by Juan de la Cierva (MINECO) program., Peer reviewed

Los avances tecnológicos tienen la capacidad de cambiar substancialmente los procesos de producción agraria, permitiendo la realización de las tareas agrícolas con mayor eficacia, precisión y de manera más sostenible. Conceptos como digitalización, tecnologías de la información y la comunicación (TICs), internet de las cosas (IoT), análisis masivo de datos (big data), aprendizaje automático (machine learning), sensores, drones, robots agrícolas y agricultura 4.0, se utilizan cada vez con mayor frecuencia en el ámbito rural, lo que está facilitando la implantación paulatina de estrategias de agricultura de precisión. En el ámbito de la sanidad vegetal, la monitorización de cultivos mediante teledetección con imágenes de alta resolución espacial permite identificar y cartografiar anomalías causadas por enfermedades y malas hierbas, y consecuentemente, diseñar estrategias de manejo para aplicar tratamientos fitosanitarios localizados y específicos según las necesidades del cultivo. En el presente artículo se contextualizan las posibilidades de las tecnologías geoespaciales en protección de cultivos y se describen varias investigaciones desarrolladas en diversos cultivos herbáceos y leñosos estratégicos de la agricultura española., Agradecemos la financiación recibida en los proyectos AGL2017-83325-C4-1R y C4-4R por parte de la Agencia Estata1 de Investigación (AEI) y el Fondo Europeo de Desarrollo Regional (FEDER).

This article belongs to the Special Issue Digital Agriculture., Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use of UAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: (1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and (2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold., This research was partly financed by the AGL2017-83325-C4-4-R (Spanish Ministry of Science and Innovation AEI/EU-FEDER funds), DECIVID and VINO ROSADO (funds from the Government of Navarra, grant nos. 0011-1365-2017-000113 and 0011-1365-2019-000111), and Intramural-CSIC (grant nos. 201840E002 and 202040E230) projects. Research of Dr. de Castro was supported by the Juan de la Cierva-Incorporación Program. Diana Marin is beneficiary of a postgraduate scholarships funded by the Universidad Pública de Navarra (FPI-UPNA-2016), and Oihane Oneka of a Youth Guarantee grant for R+D (Ministry of Science and Universities, 17/5/2018). We acknowledge the support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).

Airborne methodologies based on unmanned aerial vehicles (UAV) are becoming an extraordinary tool for implementing fast, accurate and affordable phenotyping strategies within plant breeding programs. The aim of this paper was to study the potential use of a previously developed UAV-OBIA platform, to fasten and support decision making for olive breeders regarding the selection of the most promising genotypes in terms of tree geometric traits. In particular, we have studied the feasibility of the system to efficiently classify and select olive genotypes according to four architectural parameters: tree height, crown diameter, projected crown area and canopy volume. These vegetative growth traits and their evolution during the first months after planting are key selection criteria in olive breeding programs. On-ground measurements and UAV estimations were recorded over two years (when trees were 15 and 27 months old, respectively) in two olive breeding trials using different training systems, namely intensive open vase and super high-density hedgerows. More than 1000 young trees belonging to 39 olive accessions, including new cross-bred genotypes and traditional cultivars, were assessed. Even though the accuracy in the UAV estimation compared to the on-ground measurements largely improved the second year, both methodologies detected in both years a high variability and significant differences among the studied genotypes, allowing for statistical comparisons among them. Genotype rankings based on the on-ground measures and UAV estimations were compared. The resulting Spearman’s rank coefficient correlations were very high, at above 0.85 in most cases, which highlights that very similar genotype classifications were achieved from either field-measured or airborne-estimated data. Thus, UAV imagery may be used to assess geometric traits and to develop rankings for the efficient screening and selection of genotypes in olive breeding programs., The breeding field trials in which the experiments were performed are funded by Interaceituna (Spanish Inter-Professional Association for Table Olives) through the FIUS projects PR201402347 and PRJ201703174. This research was partly financed by the AGL2017-83325-C4-4-R Project (Spanish Ministry of Science, Innovation and Universities and AEI-EU-FEDER funds), and Intramural-CSIC 201840E002 Projects. Research of A.I. de Castro was supported by the Juan de la Cierva Incorporación Program of the Spanish MINECO funds.

This article belongs to the Section Precision and Digital Agriculture., Assessing the spatial distribution of weeds within a field is a key step to the success of site-specific weed management strategies. Centaurea diluta (knapweed) is an emerging weed that is causing a major agronomic problem in southern and central Spain because of its large size, the difficulty of controlling it, and its high competitive ability. The main objectives of this study were to examine the spatial variability of C. diluta density in two wheat fields by multivariate geostatistical methods using unmanned aerial vehicle (UAV) imagery as secondary information and to delineate potential control zones for site-specific treatments based on occurrence probability maps of weed infestation. The primary variable was obtained by grid weed density field samplings, and the secondary variables were derived from UAV imagery acquired the same day as the weed field surveys. Kriging and cokriging with UAV-derived variables that displayed a strong correlation with weed density were used to compare C. diluta density mapping performance. The accuracy of the predictions was assessed by cross-validation. Cokriging with UAV-derived secondary variables generated more accurate weed density maps with a lower RMSE compare with kriging and cokriging with RVI, NDVI, ExR, and ExR(2) (the best methods for the prediction of knapweed density). Cokriged estimates were used to generate probability maps for risk assessment when implementing site-specific weed control by indicator kriging. This multivariate geostatistical approach enabled the delineation of winter wheat fields into two zones for different prescription treatments according to the C. diluta density and the economic threshold., This research was funded by the AGL2017-83325-C4-4R project (Spanish Ministry of Science, Universities and Innovation, FEDER Funds (Fondo Europeo de Desarrollo Regional).

Trabajo presentado en el Congreso en red de Olivicultura, Citricultura y Fruticultura de la Sociedad Española de Ciencias Hortícolas (CITRUSTECH), celebrado del 23 al 25 de marzo de 2021., En los últimos años, la reconstrucción tridimensional (3D) de cultivos leñosos mediante imágenes procedentes de vehículos aéreos no tripulados (UAV, por sus siglas en inglés) se ha utilizado para una caracterización eficiente de la arquitectura de la copa de cada árbol. Esta tecnología ha sido aplicada con éxito en ensayos de fenotipado de almendro y ha demostrado su potencial para plantaciones comerciales. Uno de los pasos más importantes para generar una correcta modelización 3D del cultivo es el diseño del plan de vuelo del UAV en el que, además de su altura y el solape entre imágenes, hay que definir la orientación de las pasadas de vuelo y si se combinan pasadas en diferentes direcciones. Aunque existen trabajos sobre varios aspectos del diseño de planes de vuelo-UAV, no se ha realizado ningún estudio para comprobar si el marco de plantación es otro factor a tener en cuenta, ya que las diferentes posiciones relativas de los árboles pueden provocar oclusiones en las imágenes que afectarían a la calidad de la reconstrucción 3D. Por lo anteriormente expuesto, se diseñó un plan de vuelo en cuadrícula sobre parcelas de almendro con diferentes marcos de plantación: 7×6, 6×3, 5×2, y 3,5×1,2 (medidas en metros). Las imágenes tomadas fueron procesadas para generar nubes de puntos fotogramétricas aplicando tres enfoques diferentes y utilizando: 1) sólo las imágenes tomadas en pasadas paralelas a las líneas de árboles; 2) sólo las imágenes tomadas en pasadas transversales a las líneas de árboles; 3) combinando todas las imágenes. De entre las diferentes opciones, la 3 arrojó las mayores densidades de puntos, seguida por la 1, y siendo la 2 la que originó reconstrucciones 3D de menor calidad. La diferencia en densidad de puntos entre las opciones 1 y 2 fue menor en el caso de la plantación en seto (12% menos de puntos) que en el de los marcos de plantación más amplios (20% menos de media). También se observó que la reducción en la densidad no afectó prácticamente a la reconstrucción 3D del terreno mientras que sí afectó a las copas de los almendros, lo que influyó en el posterior análisis y caracterización del cultivo. Como conclusión, se recomienda diseñar planes de vuelo que combinen pasadas paralelas y transversales a las hileras de árboles. Debido a que esto implica un mayor consumo de batería del UAV, en casos en los que haya que volar superficies extensas es preferible volar trazando pasadas paralelas a las hileras de los árboles., Los trabajos presentados han sido financiados por los proyectos AGL2017-83325-C4-4-R y Transforma (ref.: PP.TRA.TRA.2019.00.2).

Papers presented at the 13th European Conference on Precision Agriculture (Budapest, Hungary 18-22 July 2021).-- Edited by: John V. Stafford., The purpose of this research was to examine and monitor factors affecting peach cultivation as a part of an overall research program for developing Integrated Pest Management strategies. Potassium fertilization and the practice of root-collar excavation have proven effective in increasing yield and fruit quality and in extending orchard longevity on infested replant sites, respectively. Remote sensing has been proven to be an effective technology for monitoring crop conditions at field scale. Thus, hyperspectral imagery collected from a UAV was used to assess the effect on the peach spectral signature of two foliar potassium spray rates and two soil management treatments as well as the damage produced by peach rust. Significant differences in peach canopy hyperspectral profiles were observed in all analysis performed, i.e. according to the K-treatments; to the soil managements; and to the severity classes. In addition, the image wavebands that efficiently discriminated the evaluated factors were identified.

Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps—the third research contribution—which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss., This research was partly financed by the AGL2017-83325-C4-4-R Project (Spanish Ministry of Economy, Industry and Competitiveness) and EU-FEDER funds. Research of de Castro and Peña was financed by the Juan de la Cierva and Ramon y Cajal Programs, respectively. Research of Ovidiu Csillik was supported by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W1237-N23). All the fields belong to private owners, and the flights and field samplings were carried out after a written agreement had been signed. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI)., Peer reviewed

Precision viticulture has arisen in recent years as a new approach in grape production. It is based on assessing field spatial variability and implementing site-specific management strategies, which can require georeferenced information of the three dimensional (3D) grapevine canopy structure as one of the input data. The 3D structure of vineyard fields can be generated applying photogrammetric techniques to aerial images collected with Unmanned Aerial Vehicles (UAVs), although processing the large amount of crop data embedded in 3D models is currently a bottleneck of this technology. To solve this limitation, a novel and robust object-based image analysis (OBIA) procedure based on Digital Surface Model (DSM) was developed for 3D grapevine characterization. The significance of this work relies on the developed OBIA algorithm which is fully automatic and self-adaptive to different crop-field conditions, classifying grapevines, and row gap (missing vine plants), and computing vine dimensions without any user intervention. The results obtained in three testing fields on two different dates showed high accuracy in the classification of grapevine area and row gaps, as well as minor errors in the estimates of grapevine height. In addition, this algorithm computed the position, projected area, and volume of every grapevine in the field, which increases the potential of this UAV- and OBIA-based technology as a tool for site-specific crop management applications., This research was funded by the AGL2017-83325-C4-4R project (Spanish Ministry of Economy and Competition, FEDER Funds: Fondo Europeo de Desarrollo Regional). Research of A. I. de Castro and J. M. Peña was financed by the Juan de la Cierva Incorporación and Ramon y Cajal (RYC-2013-14874) Programs, respectively. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI)., Peer reviewed

This paper aims to evaluate the spatial persistence of wild oat patches in four wheat fields over time to determine the economic feasibility of using late-season wild oat maps for early site-specific weed management (SSWM) next season. The spatial persistence of wild oat patches was analyzed by three tests: land use change detection between years, spatial autocorrelation, and analysis of spreading distance. The temporal trend of wild oat patch distribution showed a clear persistence and a generalized increase in the infested area, with a noticeable level of weed aggregation and a tendency in the new weed patches to emerge close to older ones. To economically evaluate the SSWM, five simulations in four agronomic scenarios, varying wheat yields and losses due to wild oat, were conducted. When yield losses due to wild oat were minimal and for any of the expected wheat yields, some SSWM simulations were more economically profitable than the overall application in most of the fields. Nevertheless, when the yield losses due to wild oat were maximal, all SSWM simulations were less profitable than overall treatment in all the analyzed fields. Although the economic profit variations achieved with SSWM treatments were modest, any of the site-specific treatments tested are preferred to herbicide broadcast over the entire field, in order to reduce herbicide and environmental pollution., This research was funded by the AGL2017-83325-C4-4R project (Spanish Ministry of Science, Universities and Innovation, FEDER Funds: Fondo Europeo de Desarrollo Regional). Research of A.I. de Castro was financed
by the Juan de la Cierva Incorporación Program. We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI)., Peer reviewed