Under canopy light detection and ranging‐based autonomous navigation

Higuti, Vitor Akihiro Hisano; Velasquez, Andrés Eduardo Baquero; Magalhães, Daniel Varela; Becker, Marcelo; Chowdhary, Girish

doi:10.1002/rob.21852

Cited by 63 publications

(49 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…locations. Genomes to Fields is a multi-year, multi-location coordinated experiment that was planted at Furthermore, it has the capability to fit between rows in standard maize fields and can autonomously 150 follow rows using an embedded LiDar (Higuti et al, 2018). 151 Data collection by the rovers took place after all lines had reached physiological maturity (flowering) 152 but before harvest.…”

Section: Materials and Methods 125mentioning

confidence: 99%

In-field whole plant maize architecture characterized by Latent Space Phenotyping

Gage

Richards

Lepak

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Subcanopy rovers enabled 3D characterization of thousands of hybrid maize plots. Machine learning produces heritable latent traits that describe plant architecture. Rover‐based phenotyping is far more efficient than manual phenotyping. Latent phenotypes from rovers are ready for application to plant biology and breeding. Collecting useful, interpretable, and biologically relevant phenotypes in a resource‐efficient manner is a bottleneck to plant breeding, genetic mapping, and genomic prediction. Autonomous and affordable subcanopy rovers are an efficient and scalable way to generate sensor‐based datasets of in‐field crop plants. Rovers equipped with lidar can produce three‐dimensional reconstructions of entire hybrid maize (Zea mays L.) fields. In this study, we collected 2103 lidar scans of hybrid maize field plots and extracted phenotypic data from them by latent space phenotyping. We performed latent space phenotyping by two methods, principal component analysis and a convolutional autoencoder, to extract meaningful, quantitative latent space phenotypes (LSPs) describing whole‐plant architecture and biomass distribution. The LSPs had heritabilities of up to 0.44, similar to some manually measured traits, indicating that they can be selected on or genetically mapped. Manually measured traits can be successfully predicted by using LSPs as explanatory variables in partial least squares regression, indicating that the LSPs contain biologically relevant information about plant architecture. These techniques can be used to assess crop architecture at a reduced cost and in an automated fashion for breeding, research, or extension purposes, as well as to create or inform crop growth models.

show abstract

Section: Materials and Methods 125mentioning

confidence: 99%

In-field whole plant maize architecture characterized by Latent Space Phenotyping

Gage

Richards

Lepak

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the said limitation is subject to data quality not to the proposed solution. Low-cost active sensing of LiDAR technology can fill this gap in the foreseeable future [72]. Additionally, an elevation-based solution can also be underutilized in the event when crop rows and intra-row elevation differences were not captured during data acquisition and/or SfM-based processing.…”

Section: A Comparative Assessment and State-of-the-artmentioning

confidence: 99%

Integration of Remote Sensing and GIS to Extract Plantation Rows from A Drone-Based Image Point Cloud Digital Surface Model

Fareed

Rehman

2020

IJGI

View full text Add to dashboard Cite

Automated feature extraction from drone-based image point clouds (DIPC) is of paramount importance in precision agriculture (PA). PA is blessed with mechanized row seedlings to attain maximum yield and best management practices. Therefore, automated plantation rows extraction is essential in crop harvesting, pest management, and plant grow-rate predictions. Most of the existing research is consists on red, green, and blue (RGB) image-based solutions to extract plantation rows with the minimal background noise of test study sites. DIPC-based DSM row extraction solutions have not been tested frequently. In this research work, an automated method is designed to extract plantation row from DIPC-based DSM. The chosen plantation compartments have three different levels of background noise in UAVs images, therefore, methodology was tested under different background noises. The extraction results were quantified in terms of completeness, correctness, quality, and F1-score values. The case study revealed the potential of DIPC-based solution to extraction the plantation rows with an F1-score value of 0.94 for a plantation compartment with minimal background noises, 0.91 value for a highly noised compartment, and 0.85 for a compartment where DIPC was compromised. The evaluation suggests that DSM-based solutions are robust as compared to RGB image-based solutions to extract plantation-rows. Additionally, DSM-based solutions can be further extended to assess the plantation rows surface deformation caused by humans and machines and state-of-the-art is redefined.

show abstract

“…In order to deploy soft arm robotic devices in the agroforestry field setting, a robust rover is needed. The rigorously field-tested TerraSentia ( Figure 6) [60,61] is an ultra-compact (14 inches wide), ultra-light (14.5 lbs), low-cost, autonomous agbot that can navigate in a variety of field conditions autonomously. TerraSentia is (1) ultra-light-weight compared to even small robotic rovers (e.g., the 37 lb.…”

Section: Agbot Autonomous Rovers For Maneuvering Field Sitesmentioning

confidence: 99%

“…In addition, there is also the likelihood of complete signal obstruction in very dense canopies. In corn and sorghum fields, TerraSentia mitigates this issue by utilizing LIDAR-based navigation [61]. LIDAR, or Light Detection and Ranging-based units are similar to RADARs in their operation, except for the fact that they use infrared laser lights for ranging.…”

Section: Autonomous Control With Learningmentioning

confidence: 99%

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

Chowdhary

Gazzola²,

Krishnan³

et al. 2019

Sustainability

Self Cite

View full text Add to dashboard Cite

The shortage of qualified human labor is a key challenge facing farmers, limiting profit margins and preventing the adoption of sustainable and diversified agroecosystems, such as agroforestry. New technologies in robotics could offer a solution to such limitations. Advances in soft arms and manipulators can enable agricultural robots that can have better reach and dexterity around plants than traditional robots equipped with hard industrial robotic arms. Soft robotic arms and manipulators can be far less expensive to manufacture and significantly lighter than their hard counterparts. Furthermore, they can be simpler to design and manufacture since they rely on fluidic pressurization as the primary mechanisms of operation. However, current soft robotic arms are difficult to design and control, slow to actuate, and have limited payloads. In this paper, we discuss the benefits and challenges of soft robotics technology and what it could mean for sustainable agriculture and agroforestry.

show abstract

Under canopy light detection and ranging‐based autonomous navigation

Cited by 63 publications

References 48 publications

In-field whole plant maize architecture characterized by Latent Space Phenotyping

In-field whole plant maize architecture characterized by Latent Space Phenotyping

Integration of Remote Sensing and GIS to Extract Plantation Rows from A Drone-Based Image Point Cloud Digital Surface Model

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

Contact Info

Product

Resources

About