Agricultura de precisión

10 páginas y 5 figurasLa Agricultura de Precisión (AP) es un conjunto de técnicas de cultivo que utilizan tecnologías de la información para ajustar el uso de semillas y de agroquímicos en función de la diversidad del medio físico y del medio biológico. Esto conlleva una reducción de los costes de producción y una gestión agrícola más respetuosa con el medio. Este concepto es el inicio de una revolución en la gestión de los recursos naturales y en pocos años puede introducir a la agricultura a la era digital. Sin embargo, y aunque el motor del desarrollo de la AP han sido una serie de tecnologías de la era espacial (GPS, sensores, Sistemas de Información Geográfica, ...), el efecto final es, de alguna manera, volver a recuperar algunas de las características de la agricultura tradicional. En el ámbito concreto del manejo de la vegetación arvense, la detección de rodales de malas hierbas mediante el empleo de sensores o de técnicas de teledetección y la planificación y realización de tratamientos herbicidas localizados puede suponer una notable ddisminución en el consumo de herbicidas. Uno de los cuellos de botella para conseguir esta meta e smejorar nuestros conocimientos sobre la heterogeneidad espacial de las poblaciones arvenses y su dinámica temporal.N

An Approach to the Use of Depth Cameras for Weed Volume Estimation

The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.The Spanish Ministry of Economy and Competitiveness has provided support for this research via projects AGL2014-52465-C4-3-R and AGL2014-52465-C4-1-R, and Bosch Foundation. We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI)

Weed discrimination using ultrasonic sensors

A new approach is described for automatic discrimination between grasses and broad-leaved weeds, based on their heights. An ultrasonic sensor was mounted on the front of a tractor, pointing vertically down in the inter-row area, with a control system georeferencing and registering the echoes reflected by the ground or by the various leaf layers. Static measurements were taken at locations with different densities of grasses (Sorghum halepense) and broad-leaved weeds (Xanthium strumarium and Datura spp.). The sensor readings permitted the discrimination of pure stands of grasses (up to 81% success) and pure stands of broad-leaved weeds (up to 99% success). Moreover, canonical discriminant analysis revealed that the ultrasonic data could separate three groups of assemblages: pure stands of broad-leaved weeds (lower height), pure stands of grasses (higher height) and mixed stands of broad-leaved and grass weeds (medium height). Dynamic measurements confirmed the potential of this system to detect weed infestations. This technique offers significant promise for the development of real-time spatially selective weed control techniques, either as the sole weed detection system or in combination with other detection tools.This research was funded by the Spanish CICyT (project AGL 2008-04670-C03)

Assessing a fleet of robots for herbicide applications

Advanced technologies are critical for safe, site-specific and efficient control of pests (weeds, pathogens and insects) in agricultural crops. Although the scientific and technological bases of precision crop protection are mostly known and robust, the commercial application of these new technologies is still very limited. To overcome this situation, new farming methods and processes should be designed. Modern approaches rely on existing information and communication technologies (ICT) and design and construction of improved pest and crop sensors, along with enhanced pest control actuators. Mobile platforms are essential to move the needed sensors and actuators throughout the work field. Moreover, by using autonomous mobile platforms equipped with innovative perception techniques, data processing systems and tools for action, pest control procedures can be applied only if, when and where they are needed, reducing costs, environmental damages and risks for farmers. This article describes the RHEA fleet of robots highlighting the concepts and analyzing the results achieved on the application of herbicide on wheat with a spray boom.This project is funded in part by the 7th Framework Programme of the European Union under Grant Agreement No. 245986.Peer Reviewe

Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops

LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m2) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r2 = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates.This research was funded by the Spanish CICyT (Project AGL 2008-04670-C03

Cartografía de malas hierbas en cultivos de maíz mediante imágenes hiperespectrales aeroportadas (AHS)

El presente trabajo aborda la cartografía de las malas hierbas Sorghum halepense, Xanthium strumarium y Abutilon theophrasti en cultivos de maíz mediante técnicas de teledetección hiperespectral. Se ha utilizado una imagen adquirida por el sensor aeroportado AHS (Airborne Hyperspectral Scanner) con una resolución espacial en el nadir de 2,5 m y 80 bandas espectrales desde 0,43 hasta 12,5µm. La imagen fue adquirida en mayo de 2007, coincidiendo con el momento óptimo para la aplicación del herbicida, sobre una zona cultivada de maíz en la finca experimental La Poveda situada al SE de la Comunidad de Madrid. Se aplicaron diversas correcciones geométricas y radiométricas, incluida la conversión a reflectividades, que se llevó a cabo mediante un ajuste empírico basado en mediciones espectrales realizadas sobre el terreno simultáneamente a la adquisición de la imagen. La técnica de Análisis de Mezclas Espectrales (ALME) nos permitió obtener un mapa de cobertura de cada una de las malas hierbas analizadas así como información sobre las proporciones de cada cubierta (malas hierbas y maíz/suelo) en cada píxel. La validación realizada para la especie S. halepense utilizando como referencia los perímetros de los rodales obtenidos con GPS mostró que sólo un 16,8 % de la superficie ocupada por esta especie no fue discriminada a partir de la imagen.El presente trabajo ha sido realizado en el marco del proyecto “Ecología espacio-temporal y teledetección de malas hierbas en cultivos de maíz” AGL2005-06180-C03-01 financiado por el Ministerio de Ciencia e Innovación.Peer reviewe

La situación de las personas mayores en Castilla y León

Producción CientíficaAnálisis del envejecimiento como rasgo demográfico fundamental en Castilla y León, características socioeconómicas de los mayores, atención a este grupo de población, problemática y perspectivas.GeografíaObra elaborada a partir del informe encargado por el Consejo Económico y Social de Castilla y León

Discriminating Crop, Weeds and Soil Surface with a Terrestrial LIDAR Sensor

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12–14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R 2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying

Provision of ecological infrastructures to increase pollinators and other beneficial organisms in rainfed crops in Central Spain

In sustainable intensive agriculture, the biodiversity of monoculture fields can be increased by managing the field margins to provide ecological infrastructures that serve as refuges and resources for beneficial organisms (pollinators and natural enemies). In the present work we summarize two years of field trials following the goal to increase biodiversity of beneficial fauna in a barley field in Central Spain by sowing different herbaceous mixtures in the field margins. The presence of arthropods visiting flowers on plots sown with different types of seed mixtures and unsown natural flora (control plot) was compared by visual sampling every week between April and June. The results showed that a combination of herbaceous big-size seeds was the most successful mixture emerging under our experimental conditions and achieved a higher number of visits of beneficial arthropods than the unsown natural vegetation