Clustering of Laser Scanner Perception Points of Maize Plants

Reiser, David; Vázquez-Arellano, Manuel; Izard, Miguel Garrido; Paraforos, Dimitrios S.; Sharipov, Galibjon M.; Griepentrog, Hans W.

doi:10.1017/s204047001700111x

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is possible to acquire 3D data from moving a 2D laser and assembling it in 3D point clouds with a good estimation of the sensor position [8,10]. As an example, Reiser et al [17] used 3D data for clustering and identification of single plants. The algorithm measures values of plant height and stem position from a point cloud obtained from assembling the data acquired by an angled 2D LiDAR using an external total station and an Inertial Measurement Unit (IMU) for precise robot positioning.…”

Section: Introductionmentioning

confidence: 99%

Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition

Gasparino

Higuti

Velasquez

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Small robotic vehicles have been navigating agricultural fields in the pursuit of new possibilities to increase agricultural production and to meet the increasing food and energetic demands. However, a perception system with reliable awareness of the surroundings remains a challenge to achieve autonomous navigation. Camera and single-layer laser scanners have been the primary sources of information, yet the first suffers from outdoor light sensibility and both from occlusion by leaves. This paper describes a three-dimensional system acquisition for corn crops. The sensing core is a single-layer UTM30-LX laser scanner rotating around its axis, while an inertial sensor provides angular measurements. With the rotation, multiple layers are used to compose a 3D point cloud, which is represented by a two-dimensional occupancy grid. Each cell is filled according to the number of readings, and their weights derive from two procedures: firstly, a mask enhances vertical entities (stalks); secondly, two Gaussian functions on the expected position of the immediate neighboring rows weaken readings in the middle of the lane and farther rows. The resulting occupancy grid allows the representation of the cornrows by virtual walls, which are used as references to a wall follower algorithm. According to experimental results, the virtual walls are segmented with reduced influence from straying leaves and sparse weeds when compared to the segmentation done with single-layer laser scanner data. Indeed, 64.02% of 3D outputs are within 0.05 m limit error from expected lane width, while only 11.63% of single-layer laser data are within same limit.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition

Gasparino

Higuti

Velasquez

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Challenges arise for all networks when transferring them to other environments or when the growth stage of a plant (i.e., size and structure) changes, implying a high effort for training individual networks for each situation or retraining an existing network with new data according to the changes before good results can be expected [23]. Transfer learning between fruit flowers (apple, peach, and pear) worked quite well [17]; however, the transfer between different plant species and growth stages resulted in a large decrease in accuracy [21,24].…”

Section: Introductionmentioning

confidence: 99%

Approach for Image-Based Semantic Segmentation of Canopy Cover in Pea–Oat Intercropping

Munz

Reiser

2020

Agriculture

View full text Add to dashboard Cite

Intercropping systems of cereals and legumes have the potential to produce high yields in a more sustainable way compared to sole cropping systems. Their agronomic optimization remains a challenging task given the numerous management options and the complexity of interactions between the crops. Efficient methods for analyzing the influence of different management options are needed. The canopy cover of each crop in the intercropping system is a good determinant for light competition, thus influencing crop growth and weed suppression. Therefore, this study evaluated the feasibility to estimate canopy cover within an intercropping system of pea and oat based on semantic segmentation using a convolutional neural network. The network was trained with images from three datasets during early growth stages comprising canopy covers between 4% and 52%. Only images of sole crops were used for training and then applied to images of the intercropping system. The results showed that the networks trained on a single growth stage performed best for their corresponding dataset. Combining the data from all three growth stages increased the robustness of the overall detection, but decreased the accuracy of some of the single dataset result. The accuracy of the estimated canopy cover of intercropped species was similar to sole crops and satisfying to analyze light competition. Further research is needed to address different growth stages of plants to decrease the effort for retraining the networks.

show abstract

Determination of stem position and height of reconstructed maize plants using a time-of-flight camera

Vázquez-Arellano

Paraforos

Reiser

et al. 2018

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Clustering of Laser Scanner Perception Points of Maize Plants

Cited by 4 publications

References 16 publications

Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition

Improved localization in a corn crop row using a rotated laser rangefinder for three-dimensional data acquisition

Approach for Image-Based Semantic Segmentation of Canopy Cover in Pea–Oat Intercropping

Determination of stem position and height of reconstructed maize plants using a time-of-flight camera

Contact Info

Product

Resources

About