No abstract

In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5–6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations.

Sorghum halepense (johnsongrass) is a perennial weed with a vegetative reproductive system and one of the most competitive weeds in maize showing a spatial distribution in compact patches. When maize is irrigated, successive weed emergences occur in the early phenological phases of the crop, which require several herbicide applications. Our aim was to provide an accurate tool for an early detection and mapping of johnsongrass patches and delineate the actual surface area requiring a site-specific herbicide treatment based on the weed coverage. This early detection represents a major challenge in actual field scenarios because both species are in the Poaceae family, and show analogous spectral patterns, an extraordinarily similar appearance and a parallel phenological evolution. To solve this, an automatic OBIA (object-basedimage-analysis) procedure was developed to be applied on orthomosaicked images using visible (red-green-blue bands) and multispectral (red-green-blue and near infrared bands) cameras collected by an unmanned aerial vehicle (UAV) that flew at altitudes of 30, 60 and 100 m on two maize fields. One of our first phases was the generation of accurate orthomosaicked images of an herbaceous crop such as maize, which presented a repetitive pattern and nearly no invariant parameters to conduct the aerotriangulation. Here, we show that high-quality orthomosaicks were produced from both cameras and that they were able to be the first step for mapping the johnsongrass patches. The most accurate weed maps were obtained using the multispectral camera at an altitude of 30 m in both fields. These maps were then used to design a site-specific weed management program, and we demonstrated that potential herbicide savings ranged from 85 to 96 %. Our results showed that accurate and timely maps of johnsongrass patches in maize can be a key element in achieving sitespecific and sustainable herbicide applications for reducing spraying herbicides and costs.

Detección de malas hierbas en girasol en fase temprana mediante imágenes tomadas con un vehículo aéreo no tripulado (UAV) Peña, J.M. * , Torres-Sánchez, J., Serrano-Pérez, A., López-Granados, F. Departamento de Protección de Cultivos, Instituto de Agricultura Sostenible, IAS-CSIC. Córdoba, España.Resumen: La discriminación de malas hierbas en fase temprana con técnicas de teledetección requiere imágenes remotas de muy elevada resolución espacial (píxeles <5 cm). Actualmente, sólo los vehículos aéreos no tripulados (UAV) pueden generar este tipo de imágenes. El objetivo de este trabajo fue evaluar imágenes UAV tomadas con una cámara visible a diferentes alturas de vuelo (40, 60, 80 y 100 m) y cuantificar la influencia de la resolución espacial en la discriminación de malas hierbas en fase temprana en un cultivo de girasol. Se aplicó un algoritmo de clasificación de imágenes basado en objetos, el cual se divide en dos fases principales: 1) detección de líneas de cultivo y 2) clasificación de cultivo, malas hierbas y suelo desnudo. El algoritmo resultó 100% eficaz en la detección de las líneas de cultivo en todos los casos (fase 1), así como en la detección de zonas libres de mala hierba en las imágenes tomadas a 40 y 60 m de altura. En las zonas con presencia de malas hierbas, los mejores resultados se obtuvieron en las imágenes tomadas a baja altura (40 m), con un 71% de marcos de muestreo clasificados correctamente (fase 2). La mayoría de los fallos de clasificación cometidos en todas las imágenes fueron falsos negativos, es decir, malas hierbas no detectadas debido a su pequeño tamaño en el momento de la captura de las imágenes. Por tanto, el siguiente paso sería desarrollar un estudio multitemporal para estudiar la detección de las malas hierbas en estados fenológicos más avanzados. Esto podría facilitar su discriminación en las imágenes y, por tanto, disminuir el porcentaje de falsos negativos en las clasificaciones.Palabras clave: agricultura de precisión, control localizado de malas hierbas, rango espectral visible, alta resolución espacial, análisis de imágenes basado en objetos (OBIA). Weed mapping in early-season sunflower fields using images from an unmanned aerial vehicle (UAV)Abstract: Weed mapping in early season requires of very high spatial resolution images (pixels <5 cm). Currently only Unmanned Aerial Vehicles (UAV) can take such images. The aim of this work was to evaluate the optimal flight altitude for mapping weeds in an early season sunflower field using a low-cost camera that took images in the visible spectrum at several flight altitudes (40, 60, 80 and 100 m). The object based image analysis procedure used for weed mapping was divided in two main phases: 1) crop-row identification, and 2) crop, weed and bare soil classification. The algorithm identified the crop rows with 100% accuracy at every flight altitude (phase 1) and it detected weed-free zones with 100% accuracy in the images captured at 40 and 60 m flight altitude. In weed-infested zones, the classification algorithm obtained th...