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)

Effect of wheel track on the density and composition of weeds in a maize crop

El objetivo de este estudio fue analizar si la compactación producida por la rodada del tractor durante las operaciones de siembra influye en la composición y densidad de malas hierbas en cultivo de maíz. Para ello se llevó a cabo el conteo e identificación de las especies presentes en 160 unidades muestrales localizadas en la interlínea de cultivo, de las cuales la mitad estaba afectada por la rodada del tractor y la otra mitad no. El contraste de medias entre los datos con rodada y sin rodada para la riqueza de especies, densidad total y densidad por especies se realizó mediante el test de Mann-Whitney. Los resultados han puesto de manifiesto que las especies principales, a excepción de “Cyperus rotundus”, fueron significativamente más abundantes en las interlíneas con rodada. En relación a la comunidad arvense, ésta fue igualmente más diversa y abundante en las interlíneas con rodada.The aim of this study was to analyze whether the compaction caused by the tractor during sowing operations affects the composition and density of the weed flora in maize crops. For that, 160 sample units were taken in the crop interline, where half of them were affected by the tractor tread and the other half not. In each sampling unit we carried out the count and identification of weed species. The Mann-Whitney test was performed to contrast differences in species richness, total and individual species densities between tractor-tread and not tractor-tread data. The results revealed that the main species, except for “Cyperus rotundus” were significantly more abundant in the interline with tractor tread. In relation to the weed community, it was also more diverse and abundant in the interline with tractor tread

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

Accompanying weeds of a poplar energy crop under different management strategies

Se han evaluado los efectos de distintas estrategias de manejo sobre las poblaciones de malas hierbas y la productividad (i.e. biomasa) de un cultivo energético de chopo en la zona centro de España. Para ello, se han realizado dos estudios: 1) análisis durante el primer año de cultivo, el más sensible a la competencia con arvenses, repetido tres veces; y 2) valoración al cabo de los tres años de duración del turno de corte. Los resultados han puesto de manifiesto una evolución de la flora arvense con el tiempo, encontrando cambios significativos según la estrategia de manejo. Al finalizar el turno de tres años, las estrategias más productivas fueron la cubierta vegetal sembrada con “Lolium multiflorum” y la basada en herbicidas, coincidiendo con una menor competencia de malas hierbas respecto a la estrategia estándar basada en labores.We have evaluated the effects of different management strategies on weed populations and productivity (i.e. biomass) of a poplar energy crop in central Spain. To do this, two studies have been performed: 1) analysis performed during the first year of production, the most sensitive to competition with weeds, repeated three times; and 2) an assessment after the three-year harvest cycle. The results have revealed a weed flora evolution over time, finding significant changes as a function of management strategies. At the end of the three-year harvest cycle, the most productive strategies were cover crop sown with “Lolium multiflorum” and that based on herbicides, coinciding with less competition with weeds compared to the standard strategy based on tillage

3D reconstruction of weeds using depth cameras

El objetivo de este estudio fue optimizar el ángulo de posicionamiento del sensor Kinect para la reconstrucción de la estructura tridimensional de Xanthium strumarium L., Datura stramonium L. y Chenopodium album L., utilizando para ello algoritmos que permiten la captura y combinación de imágenes de profundidad y RGB. Se han comparado diferentes ángulos, fijando en cada uno de ellos el sensor Kinect de forma estática respecto de la planta objetivo. Los resultados han confirmado la correlación entre la biomasa de malas hierbas y el área estimada con el sensor. La estimación de la altura de las plantas también fue adecuada, con una media de 2cm de error dependiendo de la posición del sensor. Sin embargo, aunque el sensor ha mostrado su capacidad para la creación de modelos tridimensionales, el adecuado posicionamiento del sensor es fundamental para la correcta reconstrucción de plantas. La posición ideal del sensor debe ser elegida de acuerdo a la especie a medir y su estado fenológico. Estos resultados sugieren que Kinect es una herramienta útil para caracterizar de forma rápida y fiable las malas hierbas, con importantes ventajas sobre otros sensores debido a su bajo coste, bajo requerimiento energético y alta frecuencia de transmisión de imágenes.The objective of this study was to optimize the positioning angle of a Kinect sensor for reconstructing the three dimensional structure of weeds, using Kinect fusion algorithms to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the plant in order to obtain depth (RGB-D) images from different angles. The results confirmed the correlation between ground truth (e. g. weed biomass) and the measured area with Kinect. In addition, plant height was accurately estimated with a few centimeters error. However, although the Kinect sensor has shown its ability for plant reconstruction, proper positioning of the sensor is critical for correct reconstruction of plants. The best position of the sensor must be chosen according to the species to be measured and their growth stage. These results suggest that Kinect is a promising tool for a rapid and reliable weed characterization, with several important advantages such as low cost, low power requirement and a high frame rate

La agricultura de precisión y las TIGs en la recolección mecanizada de tomate

Las tecnologías de la información y la electrónica avanzada se están implantando en la maquinaria agrícola de forma generalizada, y la recolección mecanizada del tomate ofrece interesantes retos a resolver

Low-Cost Three-Dimensional Modeling of Crop Plants

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship

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