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Agronomy
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Facility Agriculture Robots and Autonomous Unmanned Management for Crops
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Facility Agriculture Robots and Autonomous Unmanned Management for Crops

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2611

Special Issue Editors

Dr. Wei Ma

E-Mail Website
Guest Editor
Institute of Urban Agriculture, Chinese Academy of Agriculture Sciences, Chengdu 610213, China
Interests: agricultural robots; intelligent gardening robots
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiwei Tian

E-Mail Website
Guest Editor Assistant
Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
Interests: agricultural robotics; grippers; mechanical design; machine vision; simulation

Special Issue Information

Dear Colleagues,

Facility agriculture refers to agricultural production activities carried out at artificial facilities to improve crop yield and quality and achieve year-round production. The management of facility crops should take into account efficiency and precision, striving to continuously improve yield and quality through intelligent technical means. As a global research hotspot, facility agriculture robots have played a very important role in this field, and the future will see the development of more interesting and effective facility production techniques for crops.

The application of facility agriculture robots requires, as a basis, research into agronomic mechanisms such as fruit mechanical characteristics, to avoid mechanical damage, and accurate image recognition, to reduce the number of vegetable flowers. Research into these basic agronomic theories represents the premise behind robots being used for efficient production. In the future, the ultimate goal is to build an unmanned and autonomous facility agricultural food production system to provide humans with healthier food. We hope that more scholars will publish high-quality research results to promote development in this field.

Dr. Wei Ma
Guest Editor

Dr. Zhiwei Tian
Guest Editor Assistant

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. Agronomy 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 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

  • autonomous control robot
  • intelligent agriculture
  • laser intelligent fertilization
  • agricultural sensor

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Published Papers (5 papers)

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Research

19 pages, 1661 KiB  
Article
Evaluation of the Field Performance and Economic Feasibility of Mechanized Onion Production in the Republic of Korea
by Jae-Seo Hwang and Wan-Soo Kim
Agronomy 2025, 15(7), 1721; https://doi.org/10.3390/agronomy15071721 - 17 Jul 2025
Viewed by 284
Abstract
Onion cultivation in the Republic of Korea is increasingly threatened by labor shortages and an aging rural population, underscoring the growing importance of mechanization. This study evaluated the combined and individual performances and economic feasibility of mechanized transplanting, stem cutting, harvesting, and collecting [...] Read more.
Onion cultivation in the Republic of Korea is increasingly threatened by labor shortages and an aging rural population, underscoring the growing importance of mechanization. This study evaluated the combined and individual performances and economic feasibility of mechanized transplanting, stem cutting, harvesting, and collecting operations using work efficiency; the missing plant, stem cutting, damage, and dropout rates; and foreign matter content as indicators. Mechanized operations achieved up to 358-fold higher work efficiencies than manual labor operations. However, in terms of marketability, performance was inferior due to missing plants, improperly cut stems, damaged bulbs, dropped onions, and foreign matter contamination. The economic analysis indicated that the use of individual machines is advantageous for farms larger than 10.2 ha for transplanting, 1.14 ha for stem cutting, 0 ha for harvesting (i.e., profitable regardless of farm size), and 6.95 ha for collecting. For fully mechanized operations, using machines for all four processes, the break-even area was found to be 3.63 ha, with a payback period of 2.1 years. These findings are expected to serve as a foundational reference for onion growers considering the adoption of mechanization. Full article
(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)
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14 pages, 3779 KiB  
Article
Technological Parameter Optimization of Double-Press Precision Depth-Control Seeding and Its Application in Rice Production
by Yangjie Shi, Xingye Shen, Xinhui Cheng, Jintao Xu, Jiawang Hong, Lianjie Han, Xiaobo Xi and Ruihong Zhang
Agronomy 2025, 15(7), 1704; https://doi.org/10.3390/agronomy15071704 - 15 Jul 2025
Viewed by 282
Abstract
Current rice cultivation relies on mechanical transplanting, which is costly and complex, and direct seeding, which suffers from poor quality and low efficiency. To address these issues, a double-press precision depth-control seeding method was developed in this study. Discrete element modeling (DEM) was [...] Read more.
Current rice cultivation relies on mechanical transplanting, which is costly and complex, and direct seeding, which suffers from poor quality and low efficiency. To address these issues, a double-press precision depth-control seeding method was developed in this study. Discrete element modeling (DEM) was employed to optimize key operational parameters—compaction force, soil covering cutter rotational speed, and penetration depth—using qualified seeding depth and missed seeding rates as performance metrics. Optimal results were achieved at a 60 kPa compaction force, a 300 rpm rotational speed, and a 7 cm penetration depth. A prototype seeder was manufactured and evaluated in three-year field trials against conventional dry direct seeders and mechanical transplanters. The double-press seeder demonstrated significantly superior performance compared to conventional direct seeding. It optimized the crop population structure by maintaining a high tiller number while increasing the productive tiller rate, resulting in stable annual yields exceeding 10.11 t·hm−2. Although its yield was slightly lower than that of mechanical transplanting, the double-press seeder offers a compelling practical alternative due to its operational convenience and economic benefits. Full article
(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)
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24 pages, 28445 KiB  
Article
Enhanced Multi-Threshold Otsu Algorithm for Corn Seedling Band Centerline Extraction in Straw Row Grouping
by Yuanyuan Liu, Yuxin Du, Kaipeng Zhang, Hong Yan, Zhiguo Wu, Jiaxin Zhang, Xin Tong, Junhui Chen, Fuxuan Li, Mengqi Liu, Yueyong Wang and Jun Wang
Agronomy 2025, 15(7), 1575; https://doi.org/10.3390/agronomy15071575 - 27 Jun 2025
Viewed by 227
Abstract
Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this [...] Read more.
Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). The method avoids premature convergence and improves population diversity by embedding the crossover mechanism of Differential Evolution (DE) into the Whale Optimization Algorithm (WOA) and introducing a vector disturbance strategy. It adaptively selects thresholds based on straw-covered image features. Combined with least-squares fitting, it suppresses noise and improves centerline continuity. The experimental results show that SADE-WOA accurately separates soil regions while preserving straw texture, achieving higher between-class variance and significantly faster convergence than the other tested algorithms. It runs at just one-tenth of the time of the Grey Wolf Optimizer and one-ninth of that of DE and requires only one-sixth to one-seventh of the time needed by DE-GWO. During centerline fitting, the mean yaw angle error (MEA) ranged from 0.34° to 0.67°, remaining well within the 5° tolerance required for agricultural navigation. The root-mean-square error (RMSE) fell between 0.37° and 0.73°, while the mean relative error (MRE) stayed below 0.2%, effectively reducing the influence of noise and improving both accuracy and robustness. Full article
(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)
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18 pages, 5323 KiB  
Article
Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method
by Yilin Liu, Xiang Han, Longlong Ren, Wei Ma, Baoyou Liu, Changrong Sheng, Yuepeng Song and Qingda Li
Agronomy 2025, 15(5), 1234; https://doi.org/10.3390/agronomy15051234 - 19 May 2025
Cited by 1 | Viewed by 633
Abstract
The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this [...] Read more.
The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications. Full article
(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)
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18 pages, 4939 KiB  
Article
Design and Evaluation of an Innovative Thermoelectric-Based Dehumidifier for Greenhouses
by Xiaobei Han, Tianxiang Liu, Yuliang Cai, Dequn Wang, Xiaoming Wei, Yunrui Hai, Rongchao Shi and Wenzhong Guo
Agronomy 2025, 15(5), 1194; https://doi.org/10.3390/agronomy15051194 - 15 May 2025
Viewed by 588
Abstract
Crops in greenhouses located in cold climates are frequently affected by high relative humidity (RH). This study presents the design, testing, and analysis of a dehumidifier based on thermoelectric cooling. Thermoelectric dehumidifiers (TEDs) are capable of dehumidifying greenhouses in cold regions while recovering [...] Read more.
Crops in greenhouses located in cold climates are frequently affected by high relative humidity (RH). This study presents the design, testing, and analysis of a dehumidifier based on thermoelectric cooling. Thermoelectric dehumidifiers (TEDs) are capable of dehumidifying greenhouses in cold regions while recovering heat for indoor air heating. The design of a TED is based on the specific characteristics of thermoelectric coolers (TECs). A TED consists of a cabinet, four heat exchangers, a duct fan, a water pump, and auxiliary components. The TED performance was evaluated in a Chinese solar greenhouse (CSG) with a volume of approximately 160 m3. The input voltage of the TECs, fan airflow rate, and cold-side fin area affected the TED performance, with their influence varying in magnitude. The radar chart results show that the optimal operating parameters are as follows: a fan airflow rate of 300 m3/h, a TEC input voltage of 15 V, and a cold-side fin area of 0.15 m2. With the TED running for 120 min under the optimal parameters, the RH in the CSG decreased by 25.5%, while the air temperature increased by 3.4 °C. The installation of the TED at the bottom of the CSG improved the growing environment of the crops, particularly in the vertical range between 0.2 m and 1.5 m height inside the greenhouse. These findings provide a valuable reference for applying thermoelectric cooling technology in the greenhouse field. Full article
(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)
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