Quantifying the influence of pruning treatments on olive tree architecture using UAV technology, 3D models and object-based image analysis

Premio extraordinario de Trabajo Fin de Máster curso 2015-2016. Geomática, Teledetección y Modelos Espaciales Aplicados a la Gestión ForestalOlive tree pruning is one of the most important crop management tasks and becomes a costly practice with implications in harvesting, nutrition, pest and disease control, or irrigation strategies. The type and intensity of the pruning strategy modifies the tree crown with a different degree of severity, which notably affects tree physiology, olive growing and fruit quality. In this research, it is reported a procedure based in combining UAV technology and advanced OBIA methodology to reach highthroughput quantification and detailed three-dimensional (3D) monitoring of an olive tree plantation in which their trees have been affected by three different pruning treatments (traditional, adapted and mechanized). Three flights were performed over the olive plot (before pruning, one month after pruning and almost a year after pruning) and interesting differences could be discerned among all analyzed variables for every tree: crown projected area, tree height and crown volume. The OBIA algorithm used achieved high percentages of trees correctly defined in all analyzed dates except in the areas where DSM had been generated with a lower accuracy. It was checked the olive trees located under adapted pruning, the treatment where a large amount of foliage was removed, showed the highest foliage extraction after pruning followed by trees under traditional pruning, but also experienced higher growths than the other ones, being quantified this response vegetative almost a year after pruning. The trees corresponding to the mechanized pruning treatment kept a more constant vegetative growth along the research. Due to there is almost no information about the assessment of different pruning treatments on the olive tree crown by UAV technology, this research opens the doors towards a wide study area in the agronomical sector, with interesting applications in precision oliviculture although also possibly in other woody crops

Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications

Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications

Optimizing woody crop management through automated analysis of imagery taken by unmanned aerial vehicles

La actividad agrícola ha seguido un largo proceso de evolución y de intensificación para incrementar la producción de los cultivos y satisfacer la creciente demanda de alimentos, pienso y fibra. Las técnicas empleadas en agricultura durante la “Revolución Verde” (1960-1980) tuvieron como principal objetivo el aumento de la producción sin prestar especial atención a la calidad nutricional ni a la conservación de los recursos naturales, es decir, a la sostenibilidad de la actividad agrícola. A causa de ello, en las últimas décadas han surgido inconvenientes de diversa índole relacionados, entre otros, con la contaminación de acuíferos y la erosión de suelos, la reducción notable de biodiversidad y la aparición de resistencias y nuevas plagas, enfermedades y malas hierbas. La sensibilización y la preocupación de la sociedad por la conservación del medio ambiente han originado una búsqueda del equilibrio entre producción y sostenibilidad. En este contexto, han surgido nuevas formas de agricultura sostenible entre las que destaca la Agricultura de Precisión, cuyo desarrollo se ve potenciado por la Digitalización de la Agricultura creándose un nuevo paradigma conocido como “Agricultura 4.0”. Una de las tecnologías con más proyección para este fin es la Teledetección, debido a la disponibilidad de nuevas plataformas para la adquisición de imágenes y al aumento del poder computacional de los equipos informáticos para el análisis de éstas. Ambos factores han permitido la aplicación de técnicas de análisis de imagen basado en objetos (OBIA) y aprendizaje automático, contribuyendo a resolver parte de las dificultades relacionadas con la adopción práctica de la Agricultura de Precisión. En esta Tesis Doctoral se han desarrollado un conjunto de trabajos mediante la combinación del uso de un vehículo aéreo no tripulado (UAV), modelos tridimensionales a partir de técnicas fotogramétricas y el desarrollo de procedimientos automáticos OBIA. El UAV fue equipado con sensores en distinto rango espectral para la adquisición de información de diferentes variables agronómicas y escenarios en el contexto de la agricultura de precisión. A partir de lo anterior, la presente Tesis Doctoral se ha centrado en contribuir en la generación de conocimiento para la optimización del manejo de dos cultivos leñosos de gran relevancia agronómica y socioeconómica (olivar y viñedo) de manera eficiente, económica y sostenible, principalmente en cuanto al uso sostenible de fitosanitarios. Por un lado, se ha abordado la caracterización tridimensional de ambos cultivos para llevar a cabo una monitorización multitemporal de la arquitectura de cada uno de los olivos, o cepas (en el caso de viñedo), presentes en las parcelas analizadas para la cuantificación del crecimiento vegetativo en viñedo según diferentes fechas y distintas podas en olivar, y por otro, se ha puesto a punto una metodología para la detección y cartografía de la mala hierba gramínea y perenne, conocida como grama (Cynodon dactylon L.), cuya presencia en los viñedos de diferentes zonas geográficas del país está ocasionando serios problemas de competencia y control. Los resultados obtenidos ponen de manifiesto el potencial de la combinación UAV-OBIA para llevar a cabo estrategias de manejo localizado y contribuir a la Digitalización de la Agricultura. Además, con los ajustes oportunos, los algoritmos OBIA de análisis de imágenes UAV desarrollados podrían ser adaptables y transferibles a otros cultivos leñosos, ya sea para la caracterización tridimensional con otros objetivos como el fenotipado de variedades, o para la cartografía de otras malas hierbas problemáticas o de difícil control.The agricultural activity has followed a long process of evolution and intensification to increase crop production and meet the growing demand for food, feed and fiber. The techniques used in agriculture during the "Green Revolution" (1960-1980) had as their main objective the increase of production without paying special attention neither to the nutritional quality nor to the conservation of natural resources, i.e., to the sustainability of the agricultural activity. As a result, serious problems have arisen in last decades, including water pollution and soil erosion, a significant reduction in biodiversity and the emergence of resistances and new pests, diseases and weeds. Society's awareness and concern for environmental conservation have led to a search for a balance between production and sustainability. In this context, new forms of sustainable agriculture have emerged, including Precision Agriculture, whose development is strengthened by the Digitalization of Agriculture creating a new paradigm known as "Agriculture 4.0". One of the technologies with more projection for this purpose is Remote Sensing due to the availability of new platforms for acquiring images and the increase in the computational power of computer equipment for their analysis. Both factors have allowed the application of object-based image analysis (OBIA) and automatic learning techniques, contributing to solve part of the difficulties related to the practical adoption of Precision Agriculture. In this Doctoral Thesis, a set of works have been developed by combining the use of an unmanned aerial vehicle (UAV), three-dimensional models based on photogrammetric techniques and the design of automatic OBIA procedures. The UAV was equipped with sensors in different spectral range for acquiring information of several agronomic variables and scenarios in the context of precision agriculture. On this basis, this Doctoral Thesis has focused on contributing to the generation of knowledge for the optimization of the management of two woody crops of great agronomic and socioeconomic relevance (olive orchard and vineyard) in an efficient, economic and sustainable way, mainly in terms of sustainable use of phytosanitary applications. The three-dimensional characterization of both crops has been used to carry out a multitemporal monitoring of the architecture of each one of the olive trees or vines, present in the analyzed fields, for quantifying the vegetative growth in olive orchards according to different prunings and in vineyards, and on the other hand, a methodology has been developed for detecting and mapping bermudagrass (Cynodon dactylon L.), a perennial weed whose presence in vineyards of different geographical areas of the country is causing serious problems of competition and control. The results obtained show the potential of the UAV-OBIA combination to address site-specific management strategies and contribute to the Digitization of Agriculture. In addition, it is discussed that with the appropriate adjustments, the OBIA algorithms developed for analyzing the UAV images could be adaptable and transferable to other woody crops, either for three-dimensional characterization with other objectives such as the phenotyping of varieties, or for mapping other weeds difficult to control

Agricultura de Precisión. Monitorización del cultivo mediante imágenes procedentes de UAV

Curso impartido en Granada el 26 de junio de 2017.N

Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling

[Background] Tree pruning is a costly practice with important implications for crop harvest and nutrition, pest and disease control, soil protection and irrigation strategies. Investigations on tree pruning usually involve tedious on-ground measurements of the primary tree crown dimensions, which also might generate inconsistent results due to the irregular geometry of the trees. As an alternative to intensive field-work, this study shows a innovative procedure based on combining unmanned aerial vehicle (UAV) technology and advanced object-based image analysis (OBIA) methodology for multi-temporal three-dimensional (3D) monitoring of hundreds of olive trees that were pruned with three different strategies (traditional, adapted and mechanical pruning). The UAV images were collected before pruning, after pruning and a year after pruning, and the impacts of each pruning treatment on the projected canopy area, tree height and crown volume of every tree were quantified and analyzed over time.[Results] The full procedure described here automatically identified every olive tree on the orchard and computed their primary 3D dimensions on the three study dates with high accuracy in the most cases. Adapted pruning was generally the most aggressive treatment in terms of the area and volume (the trees decreased by 38.95 and 42.05% on average, respectively), followed by trees under traditional pruning (33.02 and 35.72% on average, respectively). Regarding the tree heights, mechanical pruning produced a greater decrease (12.15%), and these values were minimal for the other two treatments. The tree growth over one year was affected by the pruning severity and by the type of pruning treatment, i.e., the adapted-pruning trees experienced higher growth than the trees from the other two treatments when pruning intensity was low (<10%), similar to the traditionally pruned trees at moderate intensity (10–30%), and lower than the other trees when the pruning intensity was higher than 30% of the crown volume.[Conclusions] Combining UAV-based images and an OBIA procedure allowed measuring tree dimensions and quantifying the impacts of three different pruning treatments on hundreds of trees with minimal field work. Tree foliage losses and annual canopy growth showed different trends as affected by the type and severity of the pruning treatments. Additionally, this technology offers valuable geo-spatial information for designing site-specific crop management strategies in the context of precision agriculture, with the consequent economic and environmental benefits.This research was funded by the AGL2014-52465-C4-4R Project (The Spanish Ministry of Economy, Industry and Competitiveness, MINECO) and the CSIC-Intramural-Project (Ref. 201640E034). The research performed by Dr. de Castro, Dr. Torres-Sánchez and Dr. Peña was financed by the Juan de la Cierva, FPI and Ramón y Cajal Programmes (Spanish MINECO funds), 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 reviewe

Monitoring the Spatial Variability of Knapweed (Centaurea diluta Aiton) in Wheat Crops Using Geostatistics and UAV Imagery: Probability Maps for Risk Assessment in Site-Specific Control

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)

Quantification of dwarfing effect of different rootstocks in ‘Picual’ olive cultivar using UAV-photogrammetry

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

Caracterización 3-D de viñas mediante imágenes procedentes de un vehículo aéreo no tripulado para el diseño de tratamientos localizados

Trabajo presentado a las II Jornadas del Grupo de Viticultura y Enología (SECH), celebradas en Madrid el 3 y 4 de noviembre de 2016.El manejo localizado de cultivos tiene como objetivo reducir la cantidad de insumos aplicados al campo, racionalizando los tratamientos según las necesidades reales derivadas de un análisis pormenorizado del cultivo. Este tipo de manejo implica un ahorro de costes para el agricultor e importantes beneficios medioambientales al reducirse el uso excesivo de agroquímicos. Antes del diseño de los tratamientos localizados es necesaria una completa y exacta caracterización morfológica del cultivo, tarea para la cual los vehículos aéreos no tripulados (conocidos como drones o UAVs) son una de las mejores opciones debido a que pueden tomar información en el momento deseado y con una resolución espacial muy elevada. En el presente trabajo se utilizó un multirrotor volando a 30 m de altura para capturar imágenes con una cámara convencional (RGB: rango visible) de bajo coste sobre dos parcelas comerciales de viña. Las imágenes fueron procesadas para obtener el modelado 3D del cultivo, tras lo que se diseñó un algoritmo de análisis de imagen completamente automatizado para la clasificación de la viña, su caracterización 3D y la detección de huecos en las hileras de cultivo. La validación de los resultados demostró una precisión cercana al 90 % en la clasificación de la viña y al 100 % en la detección de huecos. Además, la estimación de la altura de la viña tuvo un error medio cuadrático de 0,14 m.Esta investigación fue financiada por el proyecto AGL2014-52465-C4-4-R (Ministerio de Economía y Competitividad, fondos FEDER: Fondo Europeo de Desarrollo Regional). La investigación de Jorge Torres-Sánchez y el Dr. José M. Peña fue financiada por los programas FPI (BES-2012-052424) y Ramón y Cajal (MINECO), respectivamente.Peer Reviewe

Estimation of vineyard vegetative growth: analysis of 3D point cloud from unmanned aerial vehicle imagery

[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 reviewe

Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery

This article belongs to the Special Issue Application and Promotion of Unmanned Aerial System (UAS) Technology in Agriculture and Forestry.Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds.This work was partly financed by PID2020-113229RB-C44 and PID2020-113229RB-C41 (Spanish Ministry of Science and Innovation AEI/EU-FEDER funds), and Intramural-CSIC (ref.: 202040E230) projects