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Agronomy
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Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition
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Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 6336

Special Issue Editors

Prof. Dr. Jun Ni

E-Mail Website
Guest Editor
College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: agricultural smart sensor; agricultural intelligent equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Feng

E-Mail Website
Guest Editor
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: agricultural systems engineering; agricultural electrification and automation
Special Issues, Collections and Topics in MDPI journals
Dr. Lvhua Han

E-Mail Website
Guest Editor
Department of Agricultural Engineering, Jiangsu University, Zhenjiang,212013, China
Interests: agricultural equipment; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of precision operation technology and intelligent equipment in fields is the frontier of modern agricultural technology development, as the implementation of this equipment has led to the conception of adjusting measures to local conditions, the intelligent management of crop production, the maximization of the production potential of farmland, and the efficient utilization of the key factors in agricultural production and ecological environment protection. In recent years, experts have conducted much research on the interaction mechanism of crops, soil, and other environmental factors, which has caused the rapid acquisition of information, and a precise control model of crop production and intelligent equipment has been developed that uses modern information and intelligent control technology. These remarkable achievements have played an important role in updating traditional agriculture and developing modern agriculture with values of high yields, high quality, high efficiency, ecology, and safety.

This Special Issue will welcome papers that present research on the use of precision operation technology and intelligent equipment in fields. Specific topics include, but are not limited to:

  1. Agricultural sensing mechanisms and new sensors;
  2. Machine–soil–crop interaction mechanisms;
  3. Crop production control models;
  4. New agricultural equipment and field robots;
  5. Intelligent control of agricultural machinery;
  6. Unmanned operations.

Prof. Dr. Jun Ni
Dr. Lei Feng
Dr. Lvhua Han
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

  • precision operation
  • agricultural sensor
  • agricultural machinery
  • field robots
  • machine–soil–crop interaction
  • interaction mechanism
  • intelligent control
  • unmanned and automatic operations

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Related Special Issue

Published Papers (6 papers)

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Research

26 pages, 11976 KiB  
Article
Design and Test of Potato Seedling Killing and Residual Film Recycling Integrated Machine
by Yangzhou Chen, Ruofei Xing, Xiaolong Liu, Hua Zhang and Hui Li
Agronomy 2024, 14(10), 2269; https://doi.org/10.3390/agronomy14102269 - 1 Oct 2024
Viewed by 659
Abstract
Plastic film mulching technology can effectively enhance crop yield and quality, and the use of mulch has been increasing in recent years; however, the problem of mulch residue is worsening due to the large amount of recycling work and slow natural degradation. In [...] Read more.
Plastic film mulching technology can effectively enhance crop yield and quality, and the use of mulch has been increasing in recent years; however, the problem of mulch residue is worsening due to the large amount of recycling work and slow natural degradation. In this study, a potato seedling killing and residual film recycling machine is designed to provide good working conditions for potato harvesters before harvesting in response to the problems of difficult separation of film tangles, the low net rate of recycling due to the mixing of residual film with soil, and the high soil content in residual film recycling operations in northwest China. The machine is based on the potato monoculture and double row planting mode in Gansu area. This paper puts forward the overall design scheme and carries out the theoretical analysis and parameter determination of the key components, such as the seedling killing device, the film surface cleaning device, the film unloading device, and so on. Using EDEM software to carry out the virtual simulation test and Design-Expert13 to analyze the test results, we determined the optimal working scheme for the machine, with a forward speed of 0.8 m/s, a film gap of 125 mm, and a spiral stirrer speed of 600 r/min. Based on a field test for verification, the test results show that the machine’s residual film recovery rate was 83.3%, the impurity rate was 3.8%, and the rate of injury to the potatoes was 1.4%. The machine meets the requirements of national and industry standards, and it can simultaneously realize straw crushing, film surface cleaning, residual film recycling, and hydraulic film unloading operations, with better operating results and while reaching the expected results. It can also provide a reference for the design and testing of a seeding and residual film recycling machine. Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
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21 pages, 4607 KiB  
Article
HCFormer: A Lightweight Pest Detection Model Combining CNN and ViT
by Meiqi Zeng, Shaonan Chen, Hongshan Liu, Weixing Wang and Jiaxing Xie
Agronomy 2024, 14(9), 1940; https://doi.org/10.3390/agronomy14091940 - 28 Aug 2024
Viewed by 690
Abstract
Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in [...] Read more.
Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in this study. Data preprocessing is conducted using a bottleneck-structured convolutional network and a Stem module to reduce computational latency. CNNs with various kernel sizes capture local information at different scales, while the ViT network’s attention mechanism and global feature extraction enhance pest feature representation. A down-sampling method reduces the input image size, decreasing computational load and preventing overfitting while enhancing model robustness. Improved attention mechanisms effectively capture feature relationships, balancing detection accuracy and speed. The experimental results show that HCFormer achieves 98.17% accuracy, 91.98% recall, and a mean average precision (mAP) of 90.57%. Compared with SENet, CrossViT, and YOLOv8, HCFormer improves the average accuracy by 7.85%, 2.01%, and 3.55%, respectively, outperforming the overall mainstream detection models. Ablation experiments indicate that the model’s parameter count is 26.5 M, demonstrating advantages in lightweight design and detection accuracy. HCFormer’s efficiency and flexibility in deployment, combined with its high detection accuracy and precise classification, make it a valuable tool for identifying and classifying crop pests in complex environments, providing essential guidance for future pest monitoring and control. Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
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15 pages, 7121 KiB  
Article
Design of an Adaptive Height Control System for Sugarcane Harvester Header
by Meiqi Shi, Yanzhou Li, Yingchun Pan, Linfei Lu and Jin Wei
Agronomy 2024, 14(8), 1644; https://doi.org/10.3390/agronomy14081644 - 26 Jul 2024
Viewed by 769
Abstract
This study addresses the issue of low control accuracy and harvesting efficiency resulting from the manual adjustment of the header height during the sugarcane harvesting process in hilly and mountainous regions. An adaptive header height adjustment system was designed and implemented. A test [...] Read more.
This study addresses the issue of low control accuracy and harvesting efficiency resulting from the manual adjustment of the header height during the sugarcane harvesting process in hilly and mountainous regions. An adaptive header height adjustment system was designed and implemented. A test bench for the sugarcane harvester header was designed and constructed, incorporating a LiDAR to measure the ground height at the sugarcane growth point in front, and a draw-wire displacement sensor to monitor the real-time height of the header. I/O ports were allocated, and the control program was developed in the TIA Portal environment. The PLC control system achieves the precise adjustment of the cutting height based on the collected data. The experimental results indicate that the system can quickly respond and adjust the cutting height under complex terrain conditions. When the cutting height into the soil is 0 mm, the adaptive control system’s average cutting height error is 0.28 cm, and the average response time is 2.3 s. When the cutting depth into the soil is 2 cm, the average cutting height error is 0.21 cm, and the average response time is 2.31 s. Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
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17 pages, 4160 KiB  
Article
Design and Testing of Vehicle-Mounted Crop Growth Monitoring System
by Shanshan Yu, Qiang Cao, Yongchao Tian, Yan Zhu, Xiaojun Liu, Jun Ni, Wenyi Zhang and Weixing Cao
Agronomy 2024, 14(7), 1361; https://doi.org/10.3390/agronomy14071361 - 24 Jun 2024
Viewed by 752
Abstract
The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping [...] Read more.
The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping capability to achieve this goal. The system consists of a multispectral crop growth sensor, signal conditioning module, and truss-type sensor bracket with self-vibration damping capability. The commercial finite element analysis software ABAQUS 6.10 was used to conduct modal and dynamic simulation analyses of the sensor bracket, which indicate that the truss-type sensor bracket can damp vibrations effectively. The p-values (least significant differences) of crop canopy DNRE (red edge normalized difference vegetation index) under different operating speeds (1.5, 3, and 4.5 km/h) are 0.454, 0.703, 0.81, and 0.838, respectively, for four different crop growth stages. In a comparative experiment between the proposed monitoring system and two similar vehicle-mounted sensors (CropSpec and GreenSeeker RT 200) for measuring agronomic parameters at different stages of crop growth, the proposed monitoring system yielded R2 values of 0.8757, 0.7194, and 0.795, respectively, and RMSE values of 0.7157, 2.2341, and 2.0952, respectively, in the tillering stage, jointing stage, and tillering and jointing stage, outperforming the other two sensors. Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
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22 pages, 26369 KiB  
Article
Seedling-YOLO: High-Efficiency Target Detection Algorithm for Field Broccoli Seedling Transplanting Quality Based on YOLOv7-Tiny
by Tengfei Zhang, Jinhao Zhou, Wei Liu, Rencai Yue, Mengjiao Yao, Jiawei Shi and Jianping Hu
Agronomy 2024, 14(5), 931; https://doi.org/10.3390/agronomy14050931 - 28 Apr 2024
Cited by 5 | Viewed by 1471
Abstract
The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the [...] Read more.
The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the similarity between the features of broccoli root balls and soil, along with the potential for being obscured by leaves, leads to false detections of “exposed seedlings”. Additionally, features left by the end effector resemble the background, making the detection of the “missed hills” category challenging. Moreover, existing algorithms require substantial computational resources and memory. To address these challenges, we developed Seedling-YOLO, a deep-learning model dedicated to the visual detection of broccoli planting quality. Initially, we designed a new module, the Efficient Layer Aggregation Networks-Pconv (ELAN_P), utilizing partial convolution (Pconv). This module serves as the backbone feature extraction network, effectively reducing redundant calculations. Furthermore, the model incorporates the Content-aware ReAssembly of Features (CARAFE) and Coordinate Attention (CA), enhancing its focus on the long-range spatial information of challenging-to-detect samples. Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision, particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively. The model achieved a mean Average Precision of 0.5 ([email protected]) of 94.3% and a frame rate of 29.7 frames per second (FPS). In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s, Seedling-YOLO exhibited optimal efficiency and precision. It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min, meeting the requirements for real-time and precise detection. This model can be deployed on seedling replenishment robots, providing a visual solution for robots, thereby enhancing vegetable yield. Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
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20 pages, 2129 KiB  
Article
Task Allocation of Multi-Machine Collaborative Operation for Agricultural Machinery Based on the Improved Fireworks Algorithm
by Suji Zhu, Bo Wang, Shiqi Pan, Yuting Ye, Enguang Wang and Hanping Mao
Agronomy 2024, 14(4), 710; https://doi.org/10.3390/agronomy14040710 - 28 Mar 2024
Cited by 2 | Viewed by 1088
Abstract
Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning [...] Read more.
Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning and cooperative path optimization of a single operation. To address the mentioned shortcomings, this study addresses the problem of multi-machine cooperative operation of fertilizer applicators in fields with different fertility and fertilizer cooperative distribution of fertilizer trucks. The research uses the task allocation method of a multi-machine cooperative operation of applying fertilizer-transporting fertilizer. First, the problems of fertilizer applicator operation and fertilizer truck fertilizer distribution are defined, and the operating time and the distribution distance are used as optimization objectives to construct functions to establish task allocation mathematical models. Second, a Chaos–Cauchy Fireworks Algorithm (CCFWA), which includes a discretized decoding method, a population initialization with a chaotic map, and a Cauchy mutation operation, is developed. Finally, the proposed algorithm is verified by tests in an actual scenario of fertilizer being applied in the test area of Jimo District, Qingdao City, Shandong Province. The results show that compared to the Fireworks Algorithm, Genetic Algorithm, and Particle Swarm Optimization, the proposed CCFWA can address the problem of falling into a local optimum while guaranteeing the convergence speed. Also, the variance of the CCFWA is reduced by more than 48% compared with the other three algorithms. The proposed method can realize multi-machine cooperative operation and precise distribution of seeds and fertilizers for multiple seeding-fertilizer applicators and fertilizer trucks. Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
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