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Agronomy
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Unmanned Farms in Smart Agriculture
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Unmanned Farms in Smart Agriculture

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5697

Special Issue Editors

Prof. Dr. Zhiyan Zhou

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: onboard sensor design; sensor fusion; signal/image processing; agriculture; controlling system; navigation and position/orientation; autonomous take-off and landing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lian Hu

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent agricultural machinery and equipment; unmanned farm; agricultural robots; agricultural artificial intelligence

Special Issue Information

Dear Colleagues,

At present, a new round of scientific and technological revolution and industrial change is emerging; information technology, biotechnology, new materials technology and new energy technology have been widely integrated into the field of agriculture, giving rise to a large number of strategic new industries, advanced manufacturing of agricultural equipment, agricultural Internet of Things, agricultural data and agricultural robotics and other technologies gradually applied to various areas of agricultural production. Smart agriculture has also shown a strong momentum of development. Unmanned farm is an important way to achieve wisdom agriculture. Unmanned farms are supported by biotechnology, intelligent farm machinery and information technology. Biotechnology provides varieties and cultivation patterns adapted to mechanized operations for unmanned farms, intelligent farm machinery provides equipment support for automated operations of unmanned farms, and information technology provides support for precise positioning, data transmission and intelligent management of unmanned farms for farm machinery operations.

In this Special Issue, we aim to exchange knowledge on any aspect related to unmanned farms in smart agriculture, thus promoting the rapid development of agricultural mechanization and intelligence, and facilitating the construction of unmanned farms in a period of rapid development.

Prof. Dr. Zhiyan Zhou
Prof. Dr. Lian Hu
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. 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

  • smart agriculture
  • unmanned farms
  • intelligent farm machinery
  • automatic navigation
  • precision operation
  • unmanned farm
  • information technology
  • agricultural Internet of Things
  • smart management

Published Papers (5 papers)

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Research

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17 pages, 6291 KiB  
Article
Influence of Shaped Hole and Seed Disturbance on the Precision of Bunch Planting with the Double-Hole Rice Vacuum Seed Meter
by Cheng Qian, Siyu He, Wei Qin, Youcong Jiang, Zishun Huang, Meilin Zhang, Minghua Zhang, Wenwu Yang and Ying Zang
Agronomy 2024, 14(4), 768; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14040768 - 08 Apr 2024
Viewed by 376
Abstract
The double-hole rice vacuum seed meter is critical equipment for the planting precision of rice direct seeding. The effects of shaped holes and seed disturbance on the precision of rice bunch planting were investigated to improve the precision of bunch planting with the [...] Read more.
The double-hole rice vacuum seed meter is critical equipment for the planting precision of rice direct seeding. The effects of shaped holes and seed disturbance on the precision of rice bunch planting were investigated to improve the precision of bunch planting with the double-hole rice vacuum seed meter. A test bench with the rice vacuum seed meter was set up to analyze the trends in the quality of feed index, miss index, and multiple index of seed meters with different shaped holes at different speeds and vacuum pressures. Based on the optimal hole structure, different seed disturbance structures were designed to investigate the influence of the seed disturbance structure on the precision of bunch planting. A multiple linear regression model was established for the relationship between the disturbance structure, vacuum pressure, rotational speed, and the precision of bunch planting. Discrete element numerical simulation experiments were carried out to analyze the effect of disturbance structures on seeds. The planting precision of the seed meter with the shaped hole was significantly higher than that of the seed meter without the shaped hole while the shaped hole B was the optimum structure. Disturbance structure affects the quality of feed index, multiple index rate, and miss index. The planting precision of the seed disturbance structure II was better than the other structures. At a speed of 60 rpm and vacuum pressures of 2.0 kPa, 2.4 kPa, and 2.8 kPa, the qualities of feed index of seed disturbance structure II were 90%, 91.11%, and 89.17%, respectively, and the miss indexes were 2.96%, 1.94%, and 1.57%, respectively. At high rotational speeds, the precision of rice bunch planting with the seed disturbance structure is better than that without the seed disturbance structure. In the simulation test, the seed velocity and total force magnitude of the meter without disturbance structures were less than those with the disturbed structure. Simulation experiments showed that the seed disturbance structure breaks up the stacked state of seeds. Research has shown that the shaped hole holds the seed in a stable suction posture, which helps to increase the seed-filling rate. Seed disturbance improves seed mobility, thereby enhancing the precision of bunch planting. Full article
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
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25 pages, 9132 KiB  
Article
A UAV-Borne Six-Vessel Negative-Pressure Enrichment Device with Filters Designed to Collect Infectious Fungal Spores in Rice Fields
by Xiaoyan Guo, Yuanzhen Ou, Konghong Deng, Xiaolong Fan, Rui Jiang and Zhiyan Zhou
Agronomy 2024, 14(4), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14040716 - 29 Mar 2024
Viewed by 433
Abstract
Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. [...] Read more.
Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. In this study, a mobile fungal spore collection device is designed, consisting of six filters called “Capture-A”, which can collect spores and other airborne particles onto a filter located on a rotating disc of six filters that can be rotated to a position allowing for the capture of six individual samples. They are captured one at a time and designed and validated by capturing spores above the rice field, and the parameters of the key components of the collector are optimized through fluid simulation and verification experiments. The parameter combination of the “Capturer-A” in the best working state is as follows: sampling vessel filter screen with aperture size of 0.150 mm, bent air duct with inner diameter of 20 mm, negative pressure fan with 1500 Pa and spore sampling of cylindrical shape. In the field test, the self-developed “Capturer-A” was compared with the existing “YFBZ3” (mobile spore collection device made by Yunfei Co., Ltd., Zhengzhou, China). The two devices were experimented on at 15 sampling points in three diseased rice fields, and the samples were examined and counted under a microscope in the laboratory. It was found that the spores of rice blast disease and rice flax spot disease of rice were contained in the samples; the number of samples collected by a single sampling vessel of “Capturer-A” was about twice that of the device “YFBZ3”in the test. Full article
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
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18 pages, 10857 KiB  
Article
Method and Experiments for Acquiring High Spatial Resolution Images of Abnormal Rice Canopy by Autonomous Unmanned Aerial Vehicle Field Inspection
by Qiangzhi Zhang, Xiwen Luo, Lian Hu, Chuqi Liang, Jie He, Pei Wang and Runmao Zhao
Agronomy 2023, 13(11), 2731; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13112731 - 29 Oct 2023
Viewed by 1029
Abstract
The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order [...] Read more.
The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of “far-view and close-look” autonomous field inspection by unmanned aerial vehicle (UAV) to acquire HSR images of abnormal areas in the rice canopy. First, the UAV equipped with a multispectral camera flies high to scan the whole field efficiently and obtain multispectral images. Secondly, abnormal areas (namely areas with poor growth) are identified from the multispectral images, and then the geographical locations of identified areas are positioned with a single-image method instead of the most used method of reconstruction, sacrificing part of positioning accuracy for efficiency. Finally, the optimal path for traversing abnormal areas is planned through the nearest-neighbor algorithm, and then the UAV equipped with a visible light camera flies low to capture HSR images of abnormal areas along the planned path, thereby acquiring the “close-look” features of the rice canopy. The experimental results demonstrate that the proposed method can identify abnormal areas, including diseases and pests, lack of seedlings, lodging, etc. The average absolute error (AAE) of single-image positioning is 13.2 cm, which can meet the accuracy requirements of the application in this paper. Additionally, the efficiency is greatly improved compared to reconstruction positioning. The ground sampling distance (GSD) of the acquired HSR image can reach 0.027 cm/pixel, or even smaller, which can meet the resolution requirements of even leaf-scale deep-learning classification. The HSR image can provide high-quality data for subsequent automatic identification of field abnormalities such as diseases and pests, thereby offering technical support for the realization of the UAV-based automatic rice field inspection system. The proposed method can also provide references for the automatic field management of other crops, such as wheat. Full article
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
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19 pages, 11965 KiB  
Article
Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields
by Tuanpeng Tu, Lian Hu, Xiwen Luo, Jie He, Pei Wang, Li Tian, Gaolong Chen, Zhongxian Man, Dawen Feng, Weirui Cen, Mingjin Li, Yuxuan Liu, Kang Hou, Le Zi, Mengdong Yue and Yuqin Li
Agronomy 2023, 13(7), 1949; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13071949 - 23 Jul 2023
Cited by 1 | Viewed by 982
Abstract
The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are [...] Read more.
The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines. Full article
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
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Review

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32 pages, 11260 KiB  
Review
Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms
by Rui Ming, Rui Jiang, Haibo Luo, Taotao Lai, Ente Guo and Zhiyan Zhou
Agronomy 2023, 13(10), 2499; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13102499 - 28 Sep 2023
Cited by 5 | Viewed by 2131
Abstract
Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation [...] Read more.
Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings. Full article
(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)
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