Comparación de dos métodos para la estimación de los daños por sequía en pastos de dehesa / Comparative methods for assessment of dehesa pasture drought insurance in Spain

El Sistema de Seguros Agrarios con el Seguro de cobertura de los daños por sequía en los pastos aprovechados por el ganado en régimen extensivo (línea de seguro 133) aplica la teledetección mediante un índice de vegetación (NDVI), con el fin de solucionar los problemas de peritación que surgen cuando se tiene que determinar la cantidad y calidad del pasto afectado por la sequía. Por ello el seguro de cobertura de los daños por sequía en pastos es el principal instrumento para hacer frente al gasto que supone la necesidad de suplemento de alimentación del ganado reproductor debido a la sequía. En las comarcas de Vitigudino, Trujillo y Valle de los Pedroches (España) se comparó la evolución del seguro de sequía en pastos desde 2006 a 2010 con un modelo matemático de crecimiento del pasto en función de las variables ecofisiológicas y ambientales. Sumadas las decenas de sequía extrema y sequía leve, el modelo matemático contabilizó un número mayor de decenas que las proporcionadas por Agroseguro. La recomendación es comparar las curvas de crecimiento del pasto con las curvas de evolución del NDVI, para ajustar ambos modelos

Caracterizacion de la sequia en pastos anuales en Dehesas / Characterization of pasture drought in dehesas of Spain

La sequía es un término meteorológico que significa un periodo seco prolongado. El objetivo de este trabajo es caracterizar el fenómeno estacional de la sequía en pastos anuales de dehesa. Durante 2010 y 2011 se realizó un seguimiento del pasto herbáceo en El Cubo de Don Sancho (Salamanca), Trujillo (Cáceres) y Pozoblanco (Córdoba). Se midió la producción herbácea y se caracterizó botánicamente cada zona, además se midió mensualmente la variación del contenido de agua en el suelo mediante un TDR y la precipitación. Los datos de campo de precipitación, humedad del suelo y cantidad de pasto en pie, y los datos estimados de evaporación se han comparado con la evolución del índice de vegetación para seguros de sequía por teledetección determinado por Agroseguro. Los resultados mostraron un retardo entre la acumulación de agua en el suelo y el crecimiento del pasto, que se transfiere a las medidas del índice de vegetación por teledetección. En los dos años de estudio los periodos de sequía sucedieron al inicio de crecimiento, justo después de la sequía estacional típica del verano.

A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computervision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the SupportVectorMachines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the SupportVectorMachines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Tree and grazing influence on herbaceous pasture diversity in the Dehesa agroforestry system

Mediterranean Dehesas are one of the European natural habitat types of Community interest (43/92/EEC Directive), associated to high diversity levels and producer of important goods and services. In this work, tree contribution and grazing influence over pasture alpha diversity in a Dehesa in Central Spain was studied. We analyzed Richness and Shannon-Wiener (SW) indexes on herbaceous layer under 16 holms oak trees (64 sampling units distributed in two directions and in two distances to the trunk) distributed in four different grazing management zones (depending on species and stocking rate). Floristic composition by species or morphospecies and species abundance were analyzed for each sample unit. Linear mixed models (LMM) and generalized linear mixed models (GLMMs) were used to study relationships between alpha diversity measures and independent factors. Edge crown influence showed the highest values of Richness and SW index. No significant differences were found between orientations under tree crown influence. Grazing management had a significant effect over Richness and SW measures, specially the grazing species (cattle or sheep). We preliminary quantify and analyze the interaction of tree stratum and grazing management over herbaceous diversity in a year of extreme climatic conditions.

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.