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Three-Dimensional (3D) Vision and Sensing Techniques for Biological and Agricultural Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 10 September 2024 | Viewed by 2987

Special Issue Editors

Biological and Agricultural Engineering, Univerisity of Arkansas, Fayetteville, AR 72701, USA
Interests: machine vision; artificial intelligence; smart food manufacturing; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA
Interests: agricultural robotics; 2D and 3D computer vision; machine learning; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Three-dimensional (3D) vision is a powerful and useful extension of regular color cameras. With rapid developments in 3D vision hardware and analysis software algorithms, 3D vision techniques have been widely used in biological and agricultural domains, such as product quality controlling, plant phenotyping, animal behavior monitoring. Meanwhile, 3D vision sensors can be integrated into automation or robotic systems for better environmental perception.

The objective of this Special Issue is to collect high state-of-the-art research contributions, tutorials, and position papers that address the broad 3D vision challenges in biological and agricultural applications, which include, but are not limited to, 3D vision system design, conventional- or deep-learning-based RGB-D image/ point cloud data analysis, and in-field or online 3D vision applications. Original papers describing completed and unpublished work that are not currently under review by any other journal, magazine, or conference, are solicited.

Dr. Dongyi Wang
Dr. Lirong Xiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

16 pages, 9780 KiB  
Article
SOCRATES: Introducing Depth in Visual Wildlife Monitoring Using Stereo Vision
by Timm Haucke, Hjalmar S. Kühl and Volker Steinhage
Sensors 2022, 22(23), 9082; https://doi.org/10.3390/s22239082 - 23 Nov 2022
Cited by 1 | Viewed by 2490
Abstract
The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, [...] Read more.
The development and application of modern technology are an essential basis for the efficient monitoring of species in natural habitats to assess the change of ecosystems, species communities and populations, and in order to understand important drivers of change. For estimating wildlife abundance, camera trapping in combination with three-dimensional (3D) measurements of habitats is highly valuable. Additionally, 3D information improves the accuracy of wildlife detection using camera trapping. This study presents a novel approach to 3D camera trapping featuring highly optimized hardware and software. This approach employs stereo vision to infer the 3D information of natural habitats and is designated as StereO CameRA Trap for monitoring of biodivErSity (SOCRATES). A comprehensive evaluation of SOCRATES shows not only a 3.23% improvement in animal detection (bounding box mAP75), but also its superior applicability for estimating animal abundance using camera trap distance sampling. The software and documentation of SOCRATES is openly provided. Full article
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