Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping

The current lack of efficient methods for high throughput field phenotyping is a constraint on the goal of increasing durum wheat yields. This study illustrates a comprehensive methodology for phenotyping this crop's water use through the use of the two-source energy balance (TSEB) model employing very high resolution imagery. An unmanned aerial vehicle (UAV) equipped with multispectral and thermal cameras was used to phenotype 19 durum wheat cultivars grown under three contrasting irrigation treatments matching crop evapotranspiration levels (ETc): 100%ETc treatment meeting all crop water requirements (450 mm), 50%ETc treatment meeting half of them (285 mm), and a rainfed treatment (122 mm). Yield reductions of 18.3 and 48.0% were recorded in the 50%ETc and rainfed treatments, respectively, in comparison with the 100%ETc treatment. UAV flights were carried out during jointing (April 4th), anthesis (April 30th), and grain-filling (May 22nd). Remotely-sensed data were used to estimate: (1) plant height from a digital surface model (H, R2 = 0.95, RMSE = 0.18m), (2) leaf area index from multispectral vegetation indices (LAI, R2 = 0.78, RMSE = 0.63), and (3) actual evapotranspiration (ETa) and transpiration (T) through the TSEB model (R2 = 0.50, RMSE = 0.24 mm/h). Compared with ground measurements, the four traits estimated at grain-filling provided a good prediction of days from sowing to heading (DH, r = 0.58–0.86), to anthesis (DA, r = 0.59–0.85) and to maturity (r = 0.67–0.95), grain-filling duration (GFD, r = 0.54–0.74), plant height (r = 0.62–0.69), number of grains per spike (NGS, r = 0.41–0.64), and thousand kernel weight (TKW, r = 0.37–0.42). The best trait to estimate yield, DH, DA, and GFD was ETa at anthesis or during grain filling. Better forecasts for yield-related traits were recorded in the irrigated treatments than in the rainfed one. These results show a promising perspective in the use of energy balance models for the phenotyping of large numbers of durum wheat genotypes under Mediterranean conditions.info:eu-repo/semantics/publishedVersio

A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation

This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.This study is supported by the INVITE project (agreement No. 817970), funded by the Horizon 2020 Framework Program of the European Union. This study received support from a Consolidated Research Group grant awarded to the Institut de Recerca i Tecnologia Agroalimentàries (IRTA) under grant number 2021 SGR 01429 (Technologies and crop solutions for drought mitigation—AGRI DROUGHT HUB).info:eu-repo/semantics/publishedVersio

Cork oak woodland land-cover types classification: a comparison between UAV sensed imagery and field survey

This work assesses the use of aerial imagery for the vegetation cover characterization in cork oak woodlands. The study was conducted in a cork oak woodland in central Portugal during the summer of 2017. Two supervised classification methods, pixel-based and object-based image analysis (OBIA), were tested using a high spatial resolution image mosaic. Images were captured by an unmanned aerial vehicle (UAV) equipped with a red, green, blue (RGB) camera. Four different vegetation covers were distinguished: cork oak, shrubs, grass and other (bare soil and tree shadow). Results have been compared with field data obtained by the point-intercept (PI) method. Data comparison reveals the reliability of aerial imagery classification methods in cork oak woodlands. Results show that cork oak was accurately classified at a level of 82.7% with pixel-based method and 79.5% with OBIA . 96.7% of shrubs were identified by OBIA, whereas there was an overestimation of 21.7% with pixel approach. Grass presents an overestimation of 22.7% with OBIA and 12.0% with pixel-based method. Limitations rise from using only spectral information in the visible range. Thus, further research with the use of additional bands (vegetation indices or height information) could result in better land-cover type classification.info:eu-repo/semantics/acceptedVersio

Mapeo y cuantificación de las infestaciones de Orobanche crenata en guisantes mediante teledetección

Póster presentado en el XIII Congreso Nacional de Malherbología celebrado en La Laguna (Tenerife) en noviembre de 2011.Los jopos (Orobanche crenata Forsk.) son especies parásitas de cultivos leguminosos, muy extendidas en el área mediterránea (García-Torres et al., 1994). La agricultura de precisión trata de determinar y manejar la distribución espacial de factores bióticos, tales como malas hierbas y patógenos, y de factores abióticos y así fundamentar la aplicación de inputs a dosis variables, ajustados a las necesidades de pequeñas aéreas o sub-parcelas. El objetivo de este trabajo es describir brevemente la discriminación de rodales de jopos en el cultivo de guisante (Pisum sativum L.) mediante imágenes remotas multiespectrales y su manejo de precisión mediante el software SARI® (Sectioning and Assessment of Remote Images) un módulo complementario de ENVI® que divide y cuantifica la imagen de una parcela en sub-parcelas.Esta investigación se ha financiado en parte a través de los proyectos AGL2007-60926 (FEDER) y AGL2010-15506 (FEDER).Peer reviewe

Implementing intelligent asset management systems (IAMS) within an industry 4.0 manufacturing environment

9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019; Berlin; Germany; 28 August 2019 through 30 August 2019. Publicado en IFAC-PapersOnLine 52(13), p. 2488-2493This paper aims to define the different considerations and results obtained in the implementation in an Intelligent Maintenance System of a laboratory designed based on basic concepts of Industry 4.0. The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The three fundamental premises used for the development of the management system are the structuring of information, value identification and risk management

Criticality Analysis for Network Utilities Asset Management

The proposed work describes the main part of asset criticality analysis for Distribution Network Services Providers (DNSP), also known as Network Utilities, the severity-value factors definition. The methodology is based on the risk-based evaluation of assets, considering potential impacts of their failures on network value. Thus, it provides the capability to take maintenance management decision in terms of value and risk, considering the whole network under unique and homogeneous criteria. A hierarchy of assets ranked according to with value and risk will come out of this process, which represents a fundamental result serving as input of the subsequent steps of the asset management process. Specific attention is paid to network utilities issues, characterizing assets in these companies, and the services that they provide. In addition to this, high requirements established by the Service Level Agreements (SLA), that are characteristics of network services contracts, make this methodology especially suitable in this application. In order to illustrate method applicability, an example extracted from a real electrical network use case is included.Unión Europea 64573

Sectioning remote imagery for characterization of Avena sterilis infestations. Part A: Weed abundance

Software was developed to spatially assess key crop characteristics from remotely sensed imagery. Sectioning and Assessment of Remote Images (SARI ®), written in IDL ® works as an add-on to ENVI ®, has been developed to implement precision agriculture strategies. SARI ® splits field plot images into grids of rectangular >micro-images> or >micro-plots>. The micro-plot length and width were defined as multiples of the image spatial resolution. SARI ® calculates different indicators for each micro-plot, including the integrated pixel digital values. Studies on weed patches were done with SARI ® using ground-truth data and remote images of two wheat plots infested with Avena sterilis at LaFloridaII and Navajas (Southern Spain). Patches of A. sterilis represented 47.5 and 19.2% of the field areas at the two locations, respectively; the infested areas were a combination of a few large and several small patches. At LaFloridaII, 2.1% of all patches were >500 m 2 and 55.0% of all patches were smaller than 10 m 2. Based on ground-truth weed abundance data, SARI ® output includes geo-referenced and visual herbicide prescription maps, which could be used with variable-rate application equipment. © 2011 Springer Science+Business Media, LLC.This research was partially financed by the Spanish Commission of Science and Technology through the projects AGL2007-60926 and AGL2010-15506.This research was partially financed by the Spanish Commission of Science and Technology through the projects AGL2007-60926 and AGL2010-15506.Peer Reviewe