Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots Using RGB-D Frames

Magistri, Federico; Marks, Elias; Nagulavancha, Sumanth; Vizzo, Ignacio; Läbe, Thomas; Behley, Jens; Halstead, Michael; McCool, Chris; Stachniss, Cyrill

doi:10.1109/lra.2022.3193239

Cited by 25 publications

(6 citation statements)

References 59 publications

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“…This would be useful for tasks such as monitoring 3D shape completion, as performed by Magistri et al. [ 64 ] and Pan et al. [ 65 ], for use in robot automated greenhouses.…”

Section: Discussionmentioning

confidence: 99%

A 3D printed plant model for accurate and reliable 3D plant phenotyping

Bömer,

Esser,

Marks

et al. 2024

GigaScience

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Background This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. Results Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from −10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. Conclusion Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.

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“…This would be useful for tasks such as monitoring 3D shape completion, as performed by Magistri et al. [ 64 ] and Pan et al. [ 65 ], for use in robot automated greenhouses.…”

Section: Discussionmentioning

confidence: 99%

A 3D printed plant model for accurate and reliable 3D plant phenotyping

Bömer,

Esser,

Marks

et al. 2024

GigaScience

Self Cite

View full text Add to dashboard Cite

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“…Some multispectral cameras or thermal imagers can also be assembled in special card slots and integrated into the phenotype robots or other platforms on phenotype or UAV platforms. Thus, users can finish all the necessary tasks across these interfaces using preset parameters [26,[38][39][40] .…”

Section: Characteristics Of Phenotype Detection Robots 221 Structural...mentioning

confidence: 99%

Research advance in phenotype detection robots for agriculture and forestry

Wang¹,

Fan²,

Yu³

et al. 2023

International Journal of Agricultural and Biological Engineering

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The continuous development of robot technology has made phenotype detection robots a key for extracting and analyzing phenotyping data in agriculture and forestry. The different applications of agricultural robots and phenotype detection robots were discussed in this article. Further, the structural characteristics and information interaction modes of the current phenotype detection robots were summarized from the viewpoint of agriculture and forestry. The publications with keywords related to clustering distribution were analyzed and the currently available phenotype robots were classified. Additionally, a conclusion on the design criteria and evaluation system of plant phenotype detection robots was summarized and obtained, and the challenges and future development direction were proposed, which can provide a reference for the design and applications of agriculture and forestry robots.

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“…Such studies also have targeted only a single plant species to automate conventional farming and targeted only harvesting operations. Various image recognition technologies for automatic harvesting have been proposed for the farm environment and crops [23][24][25][26][27][28][29][30][31]. However, most of these technologies are targeted at conventional farming methods in which a single species is grown, thus lowering the recognition rate in environments where a variety of plants exist in small areas.…”

Section: Relevant Researchmentioning

confidence: 99%

Agricultural Robot under Solar Panels for Sowing, Pruning, and Harvesting in a Synecoculture Environment

et al. 2022

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Currently, an agricultural method called SynecocultureTM has been receiving attention as a means for multiple crop production and recovering from environmental degradation; it helps in regreening the environment and establishing an augmented ecosystem with high biodiversity. In this method, several types of plants are grown densely, and their management relies mainly on manual labor, since conventional agricultural machines and robots cannot be applied in complex vegetation. To improve work efficiency and boost regreening by scaling-up Synecoculture, we developed a robot that can sow, prune, and harvest in dense and diverse vegetation that grows under solar panels, towards the achievement of compatibility between food and energy production on a large scale. We adopted a four-wheel mechanism with sufficient ability to move on uneven terrain, and a two orthogonal axes mechanism with adjusted tool positioning while performing management tasks. In the field experiment, the robot could move straight on shelving slopes and overcome obstacles, such as small steps and weeds, and succeeded in harvesting and weeding with human operation, using the tool maneuver mechanism based on the recognition of the field situation through camera image.

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Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots Using RGB-D Frames

Cited by 25 publications

References 59 publications

A 3D printed plant model for accurate and reliable 3D plant phenotyping

A 3D printed plant model for accurate and reliable 3D plant phenotyping

Research advance in phenotype detection robots for agriculture and forestry

Agricultural Robot under Solar Panels for Sowing, Pruning, and Harvesting in a Synecoculture Environment

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