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Agriculture
Special Issues
Intelligent Equipment and Automation Technology in Farmland Production
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Intelligent Equipment and Automation Technology in Farmland Production

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 6061

Special Issue Editors

Prof. Dr. Jiale Zhao

E-Mail Website
Guest Editor
Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130025, China
Interests: precision planting; seeding monitoring; ultra-sensitive sensors; autonomous decision-making algorithms; control systems; deep learning; field image recognition; lightweight and high-strength materials
Special Issues, Collections and Topics in MDPI journals
Dr. Mingzhuo Guo

E-Mail Website
Guest Editor
College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Interests: precision agriculture; bionic design of agricultural machinery; electrospun polymer nanofibers; conductive polymer composites; functional fibrous membranes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent agricultural equipment is crucial for enhancing agricultural production efficiency and crop yield due to the fact that it can be used to provide the best growing environment for crops. In recent years, with the rapid development of high-precision multi-field coupling simulation technology, flexible sensing technology, and artificial intelligence algorithms, which have provided new ideas for the visual design of agricultural machinery and the monitoring of the entire agricultural machinery operation process, the question of how to improve the level of intelligence and informationization of agricultural machinery and equipment has become the focus of research in the field of agricultural machinery.

The main purpose of this Special Issue is to provide a platform for academic exchange and to feature innovative research results of automation technology and agricultural intelligent equipment. We invite experts and scholars engaged in related research to contribute. The contents of this Issue include, but are not limited to, the following:

  • Intelligent agricultural machinery and equipment design;
  • The application of multi-field coupling simulation technology in the design of agricultural machinery;
  • Innovative applications of high-precision control systems or related algorithms in the field of agricultural machinery;
  • Innovative applications of flexible sensors in the field of agricultural machinery.

In addition, this Special Issue welcomes research articles, review papers, and short communications that contribute to the advancement of knowledge in this rapidly developing field. We encourage submissions that highlight the design of smart agricultural equipment and automation and information control systems or related algorithms for agricultural machinery.

By publishing your research results in this Special Issue, you will have the opportunity to share your findings with a global audience and contribute to the development of the field. We look forward to receiving your submissions and facilitating the dissemination of groundbreaking research in this exciting field.

Prof. Dr. Jiale Zhao
Dr. Mingzhuo Guo
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 250 words) can be sent to the Editorial Office for assessment.

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

  • precision agriculture
  • precision planting
  • seeding monitoring
  • ultra-sensitive sensors
  • autonomous decision-making algorithms
  • control systems
  • deep learning
  • field image recognition

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

Published Papers (6 papers)

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Research

16 pages, 7752 KB  
Article
Image Segmentation of Cottony Mass Produced by Euphyllura olivina (Hemiptera: Psyllidae) in Olive Trees Using Deep Learning
by Henry O. Velesaca, Francisca Ruano, Alice Gomez-Cantos and Juan A. Holgado-Terriza
Agriculture 2025, 15(23), 2485; https://doi.org/10.3390/agriculture15232485 - 29 Nov 2025
Viewed by 275
Abstract
The olive psyllid (Euphyllura olivina), previously considered a secondary pest in Spain, is becoming more prevalent due to climate change and rising average temperatures. Its cottony wax secretions can cause substantial damage to olive crops under certain climatic conditions. Traditional monitoring [...] Read more.
The olive psyllid (Euphyllura olivina), previously considered a secondary pest in Spain, is becoming more prevalent due to climate change and rising average temperatures. Its cottony wax secretions can cause substantial damage to olive crops under certain climatic conditions. Traditional monitoring methods for this pest are often labor-intensive, subjective, and impractical for large-scale surveillance. This study presents an automatic image segmentation approach based on deep learning to detect and quantify the cottony masses produced by E. olivina in olive trees. A well-annotated image dataset is developed and published, and a thorough evaluation of current camouflaged object detection (COD) methods is carried out for this task. Our results show that deep learning-based segmentation enables accurate and non-invasive assessment of pest symptoms, even in challenging visual conditions. However, further calibration and field validation are required before these methods can be deployed for operational integrated pest management. This work establishes a public dataset and a baseline benchmark, providing a foundation for future research and decision-support tools in precision agriculture. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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25 pages, 2921 KB  
Article
Design and Application of a Portable Chestnut-Harvesting Device
by Dezhi Ren, Ruiqiang Wang, Zefei Zhang, Guolong Li, Wanyuan Huang and Wei Wang
Agriculture 2025, 15(22), 2382; https://doi.org/10.3390/agriculture15222382 - 18 Nov 2025
Viewed by 586
Abstract
To solve the problems of high resistance, high contents of impurities and high harvest damage rates commonly encountered in chestnut harvesting, a novel lightweight simplified chestnut harvester was proposed that can simultaneously perform picking, soil removal and collection. The key component of the [...] Read more.
To solve the problems of high resistance, high contents of impurities and high harvest damage rates commonly encountered in chestnut harvesting, a novel lightweight simplified chestnut harvester was proposed that can simultaneously perform picking, soil removal and collection. The key component of the harvester is the pickup drum device, which is mainly composed of a pickup claw and drum. Compared with traditional claw harvesters, the picking and impurity removal functions are combined into one. As the pickup drum device is very important in chestnut harvesters, its key components were designed and optimized in this study. According to the structure and working principle of the pickup, a mechanical simulation model based on the discrete element method (DEM) and RecurDyn 2023 was established. Through theoretical calculations and single- and multi-factor simulation tests, the optimal combination of the working parameters of the pickup drum device was obtained. The results showed that the optimal speed of the chestnut pickup drum was 45 rpm, the optimal forward speed of the chassis was 0.4 m/s, and the optimal claw length was 55 cm. A field verification test was carried out according to the optimal parameter combination. The results showed that the picking efficiency of chestnut picking device was 88.44%, and the error between this value and the simulation results (91.42%) was 1.95%—less than 3%—which verifies the correctness of the simulation model. This study provides a theoretical reference for the design and optimization of chestnut harvesters. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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25 pages, 8867 KB  
Article
DEM Simulation and Experimental Investigation of Draft-Reducing Performance of Up-Cutting Subsoiling Method Inspired by Animal Digging
by Peng Gao, Xuanting Liu, Zihe Xu, Shuo Wang, Mingzi Qu and Yunhai Ma
Agriculture 2025, 15(19), 2046; https://doi.org/10.3390/agriculture15192046 - 29 Sep 2025
Cited by 1 | Viewed by 530
Abstract
Overcoming high draft forces has long been a primary challenge in conventional subsoiling. To better utilize this agronomically advantageous technique, it is necessary to substantially reduce the draft. Inspired by the digging behaviors of fossorial animals, a low-draft up-cutting subsoiling method was proposed [...] Read more.
Overcoming high draft forces has long been a primary challenge in conventional subsoiling. To better utilize this agronomically advantageous technique, it is necessary to substantially reduce the draft. Inspired by the digging behaviors of fossorial animals, a low-draft up-cutting subsoiling method was proposed in this study. Discrete element method (DEM) simulations were employed to study the draft-reducing performance of up-cutting tools compared with regular tools. The results showed that the up-cutting motion reduced the draft by 63.07%, 63.84%, and 58.92%, respectively, at rake angles of 45°, 60°, and 75%, and by 79.73%, 63.84%, and 45.22%, respectively, at advancement velocities of 0.5 m·s−1, 1 m·s−1, and 1.5 m·s−1. An increase in up-cutting velocity reduces the draft. Soil disturbance, particle velocity distribution, and soil deformation and movement patterns change in ways that contribute to this reduction. The draft-reducing performance of a chain subsoiler developed based on the principle of soil-breaking by animal digging was verified using field tests, exhibiting a draft-reduction amplitude approaching or greater than 30%. This study shows the great application potential of the up-cutting method in reducing subsoiling drafts and provides a theoretical basis for future research. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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24 pages, 5195 KB  
Article
Design and Experimental Research on an Automated Force-Measuring Device for Plug Seedling Extraction
by Tengyuan Hou, Xinxin Chen, Jianping Hu, Wei Liu, Junpeng Lv, Youheng Tan and Fengpeng Li
Agriculture 2025, 15(18), 1939; https://doi.org/10.3390/agriculture15181939 - 13 Sep 2025
Viewed by 731
Abstract
Existing force-measuring devices lack versatility in studying the dynamic coupling process between the seedling-picking device and the plug seedling pot during automatic transplanting. This research developed a universal force-measuring device featuring a centrally symmetrical clamping needle layout and a simultaneous insertion and clamping [...] Read more.
Existing force-measuring devices lack versatility in studying the dynamic coupling process between the seedling-picking device and the plug seedling pot during automatic transplanting. This research developed a universal force-measuring device featuring a centrally symmetrical clamping needle layout and a simultaneous insertion and clamping mechanism. The force-measuring device enables the flexible adjustment of the number of clamping needles (2/3/4 needles) via a modular structure. It can also modify the insertion depth and angle of the clamping needles to accommodate three specifications of plug seedlings, namely 50-hole, 72-hole, and 128-hole plug seedlings. A real-time monitoring system with dual pull-pressure sensors is integrated to precisely acquire the dynamic response curves of the clamping force (FJ) and the disengaging force (FN) of the plug seedling pot during the seedling-picking process. Taking water spinach plug seedlings as the research object and combining with EDEM-RecurDyn coupling simulation, the interaction mechanism between the clamping needle and the plug seedling pot was elucidated. The performance of the force-measuring device was verified through systematic force-measuring experiments. The main research findings are as follows: The force-measuring device designed in this study can successfully obtain the mechanical characteristic curve of the relevant seedling plug pot throughout the automatic seedling-picking process. The simulation results show high consistency with the experimental results, indicating that the force-measuring device can effectively reveal the dynamic coupling process between the seedling-picking device and the plug seedling pot. The verification experiment demonstrates that the force-measuring device can effectively quantify the mechanical properties of the of plug seedling pots under different plug seedlings specifications and different clamping needles configurations. Reducing the hole size and increasing the number of clamping needles can effectively decrease the peak value of the disengaging force (FNmax). The peak clamping force (FJmax) is approximately inversely proportional to the needle number, with the four-needle layout providing the most uniform force distribution. The force-measuring device developed in this study is functional, applicable, and versatile, offering a general force-measuring tool and a theoretical foundation for optimal seedling-picking device design. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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16 pages, 2720 KB  
Article
Multi-Trait Phenotypic Extraction and Fresh Weight Estimation of Greenhouse Lettuce Based on Inspection Robot
by Xiaodong Zhang, Xiangyu Han, Yixue Zhang, Lian Hu and Tiezhu Li
Agriculture 2025, 15(18), 1929; https://doi.org/10.3390/agriculture15181929 - 11 Sep 2025
Viewed by 855
Abstract
In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means [...] Read more.
In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means of a SCARA robotic arm equipped with an information acquisition device consisting of an RGB camera, a depth camera, and an infrared thermal imager, high-throughput and in situ acquisition of lettuce phenotypic information can be achieved. Through semantic segmentation and point cloud reconstruction, 12 phenotypic parameters, such as lettuce plant height and crown width, were extracted from the acquired images as inputs for three machine learning models to predict fresh weight. By analyzing the training results, a Backpropagation Neural Network (BPNN) with an added feature dimension-increasing module (DE-BP) was proposed, achieving improved prediction accuracy. The R2 values for plant height, crown width, and fresh weight predictions were 0.85, 0.93, and 0.84, respectively, with RMSE values of 7 mm, 6 mm, and 8 g, respectively. This study achieved in situ, high-throughput acquisition of lettuce phenotypic information under controlled environmental conditions, providing a lightweight solution for crop phenotypic information analysis algorithms tailored for inspection tasks. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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18 pages, 6467 KB  
Article
Design and Test of a Bionic Auxiliary Soil-Crushing Device for Strip-Tillage Machines
by Kui Zhang, Yong-Ying Zhang, Xinliang Zhao, Yun Zhao, Xin Feng, Qi Wang and Jinwu Wang
Agriculture 2025, 15(9), 944; https://doi.org/10.3390/agriculture15090944 - 27 Apr 2025
Viewed by 2159
Abstract
Suitable strip-tillage effectively enhances crop productivity and soil quality in Northeast China, yet conventional strip-tillage machines suffer from inadequate soil fragmentation. To address this issue, this study developed a bionic auxiliary soil-crushing device for the equipment. Specifically, we conducted a theoretical analysis of [...] Read more.
Suitable strip-tillage effectively enhances crop productivity and soil quality in Northeast China, yet conventional strip-tillage machines suffer from inadequate soil fragmentation. To address this issue, this study developed a bionic auxiliary soil-crushing device for the equipment. Specifically, we conducted a theoretical analysis of the soil-crushing blade to identify the key structural parameters affecting operational performance, along with their optimal value ranges. The blade tooth structure was designed following the claw-toe contour of the Oriental mole cricket (Gryllotalpa orientalis) for enhanced efficiency. A two-factor (working width and working depth), three-level central composite design (CCD) experiment was carried out using EDEM 2021 discrete element simulation software, taking the soil fragmentation rate and operational resistance as response variables. The results suggested that optimal performance was achieved at a working width of 40.66 mm and a working depth of 50 mm. Field experiments demonstrate that the soil fragmentation rate increased as the operational speed rose. The addition of the auxiliary device contributed to a soil fragmentation rate of 94.54%, bringing about an 11.54% improvement compared to the non-equipped machine. This outcome also validated the accuracy of the simulation experiments. This research provides technical and equipment support for the further development of conservation tillage practices. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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