Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
3.1
Journals
Horticulturae
Special Issues
Advanced Automation for Tree Fruit Orchards and Vineyards
share announcement

Advanced Automation for Tree Fruit Orchards and Vineyards

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3006

Special Issue Editors

Dr. Tao Li

E-Mail Website
Guest Editor
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
Interests: agricultural robotics; visual servoing; nonlinear control; intelligence task planning; reinforcement learning
Prof. Dr. Hui Zhao

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China
Interests: agricultural robot control technology; intelligent control theory and applications; new energy control technology and applications
Dr. Zhengqiang Fan

E-Mail
Guest Editor
College of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing, China
Interests: mobile robotics; agricultural plant science; environmental information perception; information fusion; autonomous navigation
Dr. Qingchun Feng

E-Mail Website
Guest Editor
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
Interests: agricultural robotics control; compliant operation; intelligence sensing technology; deep reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the global population grows and consumer demand increases, enhancing production efficiency and product quality in orchards and vineyards has become a focal point within the agricultural tech community. For many countries and regions, orchards and vineyards are not merely segments of agricultural production; they also symbolize essential cultural heritage, economic revenue, and tourism assets.

Over the past few decades, with technological advancements, agriculture has transitioned from traditional manual labor to modernization, mechanization, and automation. Especially within the unique agricultural spheres of orchards and vineyards, where labor costs are high, physical demands are substantial, and timely needs are critical, the application of automation technologies is especially pertinent. From basic drip irrigation systems and automatic fertilization to advanced drone monitoring, fruit identification, and automated harvest robots, the implementation of these technologies has shown significant outcomes in some developed areas. However, despite their vast potential, their global adoption still faces numerous technological and cultural challenges.

This Special Issue aims to delve deeply into the current status of and developmental trends in automation technologies in orchards and vineyards. It showcases how these can assist fruit growers and winemakers in managing their farms more efficiently, accurately, and economically. Key areas of exploration include the following:

Sensing technologies: Highlighting the latest sensing technologies that aid growers in the real-time monitoring of tree health, fruit status, and soil conditions.

  • Robots and automated harvesting: Introducing cutting-edge automation and robotic technologies that address harvesting and management challenges within the orchard and vineyard landscapes.
  • Data-driven orchard management: Exploring the utilization of big data and machine learning for intelligent decision support in orchards and vineyards.
  • Disease and pest management: Discussing how automation technologies play a role in preventing and controlling diseases as well as pests in orchards and vineyards.
  • Sustainability and sustainable agriculture: Delving into how advanced automation technologies ensure environmentally friendly and economically sustainable practices in orchards and vineyards.

We cordially invite scholars, researchers, fruit growers, winemakers, and all those interested in agricultural automation technologies to submit their research, case studies, reviews, and perspectives, anticipating the collective sharing and exploration of the latest advancements in automation for orchards and vineyards within this Special Issue.

Dr. Tao Li
Prof. Dr. Hui Zhao
Dr. Zhengqiang Fan
Dr. Qingchun Feng
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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

  • automated harvesting
  • robotics in agriculture
  • sensing technologies
  • machine learning in agriculture

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 9827 KiB  
Article
MSOAR-YOLOv10: Multi-Scale Occluded Apple Detection for Enhanced Harvest Robotics
by Heng Fu, Zhengwei Guo, Qingchun Feng, Feng Xie, Yijing Zuo and Tao Li
Horticulturae 2024, 10(12), 1246; https://doi.org/10.3390/horticulturae10121246 - 25 Nov 2024
Viewed by 295
Abstract
The accuracy of apple fruit recognition in orchard environments is significantly affected by factors such as occlusion and lighting variations, leading to issues such as missed and false detections. To address these challenges, particularly related to occluded apples, this study proposes an improved [...] Read more.
The accuracy of apple fruit recognition in orchard environments is significantly affected by factors such as occlusion and lighting variations, leading to issues such as missed and false detections. To address these challenges, particularly related to occluded apples, this study proposes an improved apple-detection model, MSOAR-YOLOv10, based on YOLOv10. Firstly, a multi-scale feature fusion network is enhanced by adding a 160 × 160 feature scale layer to the backbone network, which increases the model’s sensitivity to small local features, particularly for occluded fruits. Secondly, the Squeeze-and-Excitation (SE) attention mechanism is integrated into the C2fCIB convolution module of the backbone network to improve the network’s focus on the regions of interest in the input images. Additionally, a Diverse Branch Block (DBB) module is introduced to enhance the performance of the convolutional neural network. Furthermore, a Normalized Wasserstein Distance (NWD) loss function is proposed to effectively reduce missed detections of densely packed and overlapping targets. Experimental results in orchards indicate that the proposed improved YOLOv10 model achieves precision, recall, and mean average precision rates of 89.3%, 89.8%, and 92.8%, respectively, representing increases of 3.1%, 2.2%, and 3.0% compared to the original YOLOv10 model. These results validate that the proposed network significantly enhances apple recognition accuracy in complex orchard environments, particularly improving the operational precision of harvesting robots in real-world conditions. Full article
(This article belongs to the Special Issue Advanced Automation for Tree Fruit Orchards and Vineyards)
►▼ Show Figures

Figure 1

16 pages, 3020 KiB  
Article
Active Laser-Camera Scanning for High-Precision Fruit Localization in Robotic Harvesting: System Design and Calibration
by Kaixiang Zhang, Pengyu Chu, Kyle Lammers, Zhaojian Li and Renfu Lu
Horticulturae 2024, 10(1), 40; https://doi.org/10.3390/horticulturae10010040 - 31 Dec 2023
Cited by 3 | Viewed by 2076
Abstract
Robust and effective fruit detection and localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving fruit detection, less emphasis has been placed on the fruit localization aspect, which is a crucial yet challenging task due to [...] Read more.
Robust and effective fruit detection and localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving fruit detection, less emphasis has been placed on the fruit localization aspect, which is a crucial yet challenging task due to limited depth accuracy from existing sensor measurements in the natural orchard environment with variable lighting conditions and foliage/branch occlusions. In this paper, we present the system design and calibration of an Active LAser-Camera Scanner (ALACS), a novel perception module for robust and high-precision fruit localization. The hardware of the ALACS mainly consists of a red line laser, an RGB camera, and a linear motion slide, which are seamlessly integrated into an active scanning scheme where a dynamic-targeting laser-triangulation principle is employed. A high-fidelity extrinsic model is developed to pair the laser illumination and the RGB camera, enabling precise depth computation when the target is captured by both sensors. A random sample consensus-based robust calibration scheme is then designed to calibrate the model parameters based on collected data. Comprehensive evaluations are conducted to validate the system model and calibration scheme. The results show that the proposed calibration method can detect and remove data outliers to achieve robust parameter computation, and the calibrated ALACS system is able to achieve high-precision localization with the maximum depth measurement error being less than 4 mm at distance ranging from 0.6 to 1.2 m. Full article
(This article belongs to the Special Issue Advanced Automation for Tree Fruit Orchards and Vineyards)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop