BUSCADOR RECOLECTA

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).

Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m²) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches., This research was funded by the Spanish Ministry of Economy and Competitiveness
(MINECO) under Project AGL2014-52465-C4-1-R. We thank the Ministry and also David Campos and
José Manuel Martín for the substantial task of processing the images. The contributions of Alice E. Milne and
Jonathan Storkey form part the Soil to Nutrition (S2N) strategic programme (BBS/E/C/000I0330) funded by
the Biological Sciences Research Council (BBSRC) and NE/N018125/1 LTS-M ASSIST - Achieving Sustainable
Agricultural Systems, funded by NERC and BBSRC (BBS/E/C/000I0140) of the United Kingdom.

Durante los últimos años el
desarrollo de nuevas tecnologías
para la agricultura de precisión
ha alcanzado un considerable
grado de madurez. En el ámbito
específico de la gestión de las
malas hierbas se han desarrollado
numerosas tecnologías tanto
de detección como de actuación.
A los procedimientos disponibles
para la detección desde
satélite o d,esde avión se han
unido otros mucho más precisos
para la detección desde drones
volando a baja altura. Por otro
lado, la disponibilidad actual
de sensores y cámaras 2D y 3D
de muy alta resolución y de ordenadores
más rápidos capaces
de procesar grandes volúmenes
de datos abre la posibilidad de
llevar a cabo actuaciones en
tiempo real. En este trabajo se
expone una breve síntesis de
los últimos avances producidos
en sistemas de detección aérea
y terrestre así como de algunos
de los equipos inteligentes disponibles
para el control físico
o químico de las malas hierbas
detectadas., Parte de los trabajos expuestos están siendo financiados
por los proyectos AGL2014-52465-C4-1R; C4-3R
y C4-4R integrados todos en un proyecto coordinado
(Ministerio de Economía y Competitividad, fondos
FEDER: Fondo Europeo de Desarrollo Regional)., Peer reviewed