A Low-Cost Approach to Automatically Obtain Accurate 3D Models of Woody Crops

Bengochea-Guevara, José M.; Andújar, Dionisio; Sanchez-Sardana, Francisco L.; Cantuña, Karla; Ribeiro, Ángela

doi:10.3390/s18010030

Cited by 18 publications

(16 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Next step was conducted with a variant of the iterative closest point (ICP) algorithm, which provides a point cloud as output. Thus, the modified algorithm creates a point cloud by detecting the overlapping areas in sequential frames by assessing the relative position of the Kinect sensor for each frame to create a 3D model and removing outliers from the mesh [26]. Outliers could appear isolated in the point cloud.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

Rueda-Ayala

Peña

Höglind

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

Pastures are botanically diverse and difficult to characterize. Digital modeling of pasture biomass and quality by non-destructive methods can provide highly valuable support for decision-making. This study aimed to evaluate aerial and on-ground methods to characterize grass ley fields, estimating plant height, biomass and volume, using digital grass models. Two fields were sampled, one timothy-dominant and the other ryegrass-dominant. Both sensing systems allowed estimation of biomass, volume and plant height, which were compared with ground truth, also taking into consideration basic economical aspects. To obtain ground-truth data for validation, 10 plots of 1 m2 were manually and destructively sampled on each field. The studied systems differed in data resolution, thus in estimation capability. There was a reasonably good agreement between the UAV-based, the RGB-D-based estimates and the manual height measurements on both fields. RGB-D-based estimation correlated well with ground truth of plant height ( R 2 > 0.80 ) for both fields, and with dry biomass ( R 2 = 0.88 ), only for the timothy field. RGB-D-based estimation of plant volume for ryegrass showed a high agreement ( R 2 = 0.87 ). The UAV-based system showed a weaker estimation capability for plant height and dry biomass ( R 2 < 0.6 ). UAV-systems are more affordable, easier to operate and can cover a larger surface. On-ground techniques with RGB-D cameras can produce highly detailed models, but with more variable results than UAV-based models. On-ground RGB-D data can be effectively analysed with open source software, which is a cost reduction advantage, compared with aerial image analysis. Since the resolution for agricultural operations does not need fine identification the end-details of the grass plants, the use of aerial platforms could result a better option in grasslands.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The latter methodology included height selection and RGB segmentation, using a unique model for plant discrimination. Combination of various frames allows reconstruction of big crop surface areas [21,26]. Live use of RGB-D on outdoors scenarios is possible with the current version of Kinect ® v2.…”

Section: Introductionmentioning

confidence: 99%

Comparing UAV-Based Technologies and RGB-D Reconstruction Methods for Plant Height and Biomass Monitoring on Grass Ley

Rueda-Ayala

Peña

Höglind

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…As aforementioned, the LIDAR resolution is greatly affected by the vehicle speed, so it traveled throughout the readings at constant speed below 3 Km h-1, maintaining a steady course without maneuverings to follow a straight path. This is a key factor for obtaining accurate measurements [23]. Low speed in conjunction with its electric motor enables a vibration-free motion, which is highly convenient for high-quality information acquisition.…”

Section: Resultsmentioning

confidence: 99%

“…Training structures were later removed by subtracting their volume from the totality (horizontal wires were discarded as they have a negligible effect in the volume calculations). Finally, data points that were considered outliers (those whose average distance to its 64 neighbors is greater than the standard deviation of the distance to the neighbors of all the points) were removed automatically from the point cloud using [23]. After filtering, the output data represented only the vine shoot geometry for every 10-vine batch.…”

Section: Data Processingmentioning

confidence: 99%

On-Ground Vineyard Reconstruction Using a LiDAR-Based Automated System

Moreno

Valero

Bengochea-Guevara

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Crop 3D modeling allows site-specific management at different crop stages. In recent years, light detection and ranging (LiDAR) sensors have been widely used for gathering information about plant architecture to extract biophysical parameters for decision-making programs. The study reconstructed vineyard crops using light detection and ranging (LiDAR) technology. Its accuracy and performance were assessed for vineyard crop characterization using distance measurements, aiming to obtain a 3D reconstruction. A LiDAR sensor was installed on-board a mobile platform equipped with an RTK-GNSS receiver for crop 2D scanning. The LiDAR system consisted of a 2D time-of-flight sensor, a gimbal connecting the device to the structure, and an RTK-GPS to record the sensor data position. The LiDAR sensor was facing downwards installed on-board an electric platform. It scans in planes perpendicular to the travel direction. Measurements of distance between the LiDAR and the vineyards had a high spatial resolution, providing high-density 3D point clouds. The 3D point cloud was obtained containing all the points where the laser beam impacted. The fusion of LiDAR impacts and the positions of each associated to the RTK-GPS allowed the creation of the 3D structure. Although point clouds were already filtered, discarding points out of the study area, the branch volume cannot be directly calculated, since it turns into a 3D solid cluster that encloses a volume. To obtain the 3D object surface, and therefore to be able to calculate the volume enclosed by this surface, a suitable alpha shape was generated as an outline that envelops the outer points of the point cloud. The 3D scenes were obtained during the winter season when only branches were present and defoliated. The models were used to extract information related to height and branch volume. These models might be used for automatic pruning or relating this parameter to evaluate the future yield at each location. The 3D map was correlated with ground truth, which was manually determined, pruning the remaining weight. The number of scans by LiDAR influenced the relationship with the actual biomass measurements and had a significant effect on the treatments. A positive linear fit was obtained for the comparison between actual dry biomass and LiDAR volume. The influence of individual treatments was of low significance. The results showed strong correlations with actual values of biomass and volume with R2 = 0.75, and when comparing LiDAR scans with weight, the R2 rose up to 0.85. The obtained values show that this LiDAR technique is also valid for branch reconstruction with great advantages over other types of non-contact ranging sensors, regarding a high sampling resolution and high sampling rates. Even narrow branches were properly detected, which demonstrates the accuracy of the system working on difficult scenarios such as defoliated crops.

show abstract

“…Se concluyó que los sensores RGB-D podrían reemplazar sistemas láser en algunos de los escenarios de fenotipado. En [22] y [23] se reconstruyeron estructuras arbóreas en viñedo y chopos estimando el Índice de Área Foliar (LAI) y su volumen demostrando el potencial de estos sensores para la reconstrucción tridimensional.…”

Section: Introductionunclassified