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Agriculture
Special Issues
Research Advances in Perception for Agricultural Robots
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Research Advances in Perception for Agricultural Robots

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 2263

Special Issue Editors

Dr. Hanwen Kang

E-Mail Website
Guest Editor
College of Engineering, South China Agriculture University, Guangzhou 510070, China
Interests: robotics; robotic vision; deep learning; autonomous navigation; field robot; robot planning
Special Issues, Collections and Topics in MDPI journals
Dr. Hugh Zhou

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Monash University, Clayton, VIC 3800, Australia
Interests: agricultural robotics; adaptive grasping; soft robotics; robot sensing and perception
Dr. Yaohui Chen

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: robotics; agricultural robot; machine learning; computer vision; phenotyping; digital orchard

Special Issue Information

Dear Colleagues,

Agricultural robots are becoming an integral part of the farming industry, offering solutions to shortages, and improving production efficiency. However, integrating them into existing farming practices or exploring new agricultural applications presents challenges, requiring environmental awareness, adaptability to variations and compatibility with existing systems. The complexity of the agricultural environment, characterized by unstructured nature and other factors, is a major challenge. Safety for operators and crops is also vital/ critical, especially in dynamic and complex environments. Overcoming these challenges is crucial for realizing the full potential of agricultural robots.

This Special Issue aims to publish original, peer-reviewed papers on state-of-the-art smart sensing and perception technologies and their practical applications in agricultural robotic systems. These papers will encompass various artificial intelligence methodologies applied to different robotic platforms, including flying and ground-based systems, with a particular focus on their advanced perceptual and cognitive capabilities. These technologies will be explored for applications in smart agriculture, spanning livestock, horticulture, forestry and related fields. Articles related to smart robots in agriculture are encouraged, highlighting innovations in software and hardware development. Submissions in the form of original research, short communications and comprehensive reviews are welcome.

Dr. Hanwen Kang
Dr. Hugh Zhou
Dr. Yaohui Chen
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. Agriculture 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.

Keywords

  • robotics
  • machine learning
  • deep learning
  • computer vision
  • perception
  • navigation
  • manipulation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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24 pages, 41622 KiB  
Article
Picking-Point Localization Algorithm for Citrus Fruits Based on Improved YOLOv8 Model
by Yun Liang, Weipeng Jiang, Yunfan Liu, Zihao Wu and Run Zheng
Agriculture 2025, 15(3), 237; https://doi.org/10.3390/agriculture15030237 - 22 Jan 2025
Viewed by 480
Abstract
The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus [...] Read more.
The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus picking-point localization, named as CPPL. The CPPL is achieved based on two stages, namely the detection stage and the segmentation stage. For the detection stage, we define the KD-YOLOP to accurately detect citrus fruits to quickly localize the initial picking region. The KD-YOLOP is defined based on a knowledge distillation learning and a model pruning to reduce the computational cost while having a competitive accuracy. For the segmentation stage, we define the RG-YOLO-seg to efficiently segment the citrus branches to compute the picking points. The RG-YOLO-seg is proposed by introducing the RGNet to extract efficient features and using the GSNeck to fuse multi-scale features. Therefore, by using knowledge distillation, model pruning, and a lightweight model for branch segmentation, the proposed CPPL achieves accurate real-time localization of citrus picking points. We conduct extensive experiments to evaluate our method; many results show that the proposed CPPL outperforms the current methods and achieves adequate accuracy. It provides an efficient and robust novel method for real-time citrus harvesting in practical agricultural applications. Full article
(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)
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18 pages, 8385 KiB  
Article
Accurate Fruit Phenotype Reconstruction via Geometry-Smooth Neural Implicit Surface
by Wei Ying, Kewei Hu, Ayham Ahmed, Zhenfeng Yi, Junhong Zhao and Hanwen Kang
Agriculture 2024, 14(12), 2325; https://doi.org/10.3390/agriculture14122325 - 19 Dec 2024
Viewed by 611
Abstract
Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct [...] Read more.
Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct phenotyping of plants in farm environments. This study investigates a learning-based phenotyping method using neural implicit surfaces reconstruction to achieve accurate in situ phenotyping of pepper plants in greenhouse environments. To quantitatively evaluate the performance of this method, traditional point cloud registration on 3D scanning data is implemented for comparison. Experimental result shows that NIR (neural implicit surfaces reconstruction) achieves competitive accuracy compared to the 3D scanning method. The mean distance error between the scanner-based method and the NeRF (neural radiance fields)-based method is 0.811 mm. This study shows that the learning-based NeRF method has similar accuracy to the 3D scanning-based method but with greater scalability and faster deployment capabilities. Full article
(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)
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Review

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23 pages, 4461 KiB  
Review
Trends and Applications of Computed Tomography in Agricultural Non-Destructive Testing
by Qi Wang, Hui Xue, Jerome Jeyakumar John Martin, Mingming Hou, Hongxing Cao, Zhiguo Dong, Jianshe Li and Chengxu Sun
Agriculture 2024, 14(12), 2329; https://doi.org/10.3390/agriculture14122329 - 19 Dec 2024
Viewed by 610
Abstract
With the continuous progress of technology, computed tomography (CT) technology has expanded from medicine to agriculture and other industries. With the advantages of non-destructiveness, high resolution, and high precision, CT technology shows great application potential in the agricultural field. However, there are still [...] Read more.
With the continuous progress of technology, computed tomography (CT) technology has expanded from medicine to agriculture and other industries. With the advantages of non-destructiveness, high resolution, and high precision, CT technology shows great application potential in the agricultural field. However, there are still some problems with this technology that need to be solved. This paper aims to show the application of CT technology in the agricultural field, find technical challenges, and put forward specific countermeasures, so that CT technology can be better applied in the agricultural field. This paper summarizes the application of CT technology in the quality detection of agricultural products, disease and insect pest identification, seed screening, soil analysis, and precision agriculture management, and focuses on the current challenges and the countermeasures, and looks into the role of this technology in promoting agricultural development in the future. Despite various challenges, CT technology has far more advantages than disadvantages, and it is expected to become an indispensable part of all the links of agricultural production and promote the development of precision agriculture and smart agriculture. Full article
(This article belongs to the Special Issue Research Advances in Perception for Agricultural Robots)
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