Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.9
3.3
Journals
Agriculture
Special Issues
Intelligent Agricultural Machinery Design for Smart Farming
share announcement

Intelligent Agricultural Machinery Design for Smart Farming

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

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 3340

Special Issue Editor

Prof. Dr. Changyou Li

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent agricultural machinery; intelligent measurement and control algorithm; drying equipment; remoting monitor of agricultural machinery; analytical theory of dryingsing technology

Special Issue Information

Dear Colleagues,

Intelligent agricultural machinery design serves as a cornerstone of smart agriculture, with the primary aim of leveraging cutting-edge technology and automation systems to optimize the efficiency and quality of agricultural production. The innovation encompasses a range of agricultural machinery and equipment, including tractors, planters, harvesters, and irrigation systems. By seamlessly integrating sensors, artificial intelligence, data analytics, and communication technology, smart agricultural machinery facilitates intelligent monitoring and automated control of field operations.

The central objective of smart agricultural machinery design is to enhance the efficiency and sustainability of agricultural production. Through real-time monitoring and analysis of critical data such as soil moisture levels, crop growth conditions, and weather patterns, smart agricultural machinery can dynamically adjust field management activities such as planting, irrigation, and fertilization to align with actual conditions. This adaptive approach maximizes crop yields while minimizing resource wastage.

Moreover, smart agricultural machinery design contributes to improved working conditions for agricultural laborers and reduces their overall workload. Automation systems replace traditional manual tasks, resulting in reduced labor requirements, enhanced operational efficiency, and a decreased risk of human error. Consequently, agricultural production becomes safer and more reliable.

This Special Issue of Agriculture welcomes novel works regarding the use of intelligent agricultural machinery for smart farming, without any restrictions on their applications.

Prof. Dr. Changyou Li
Guest Editor

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. 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

  • smart agriculture
  • drying technology
  • smart farm machinery
  • modern agriculture
  • post harvesting process

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 (4 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

19 pages, 3476 KiB  
Article
Parameter Calibration and Experimental Verification of the Discrete Element Model of the Edible Sunflower Seed
by Xuefeng Zhu, Yang Xu, Changjie Han, Binning Yang, Yan Luo, Shilong Qiu, Xiaona Huang and Hanping Mao
Agriculture 2025, 15(3), 292; https://doi.org/10.3390/agriculture15030292 - 29 Jan 2025
Viewed by 444
Abstract
A discrete element model of the edible sunflower seed was constructed, addressing the lack of an accurate model for edible sunflower seeds in the simulation process of seeding, cleaning, and transportation, and it was calibrated and verified through actual and simulation tests. Taking [...] Read more.
A discrete element model of the edible sunflower seed was constructed, addressing the lack of an accurate model for edible sunflower seeds in the simulation process of seeding, cleaning, and transportation, and it was calibrated and verified through actual and simulation tests. Taking the edible sunflower seed as the research object, the range of its simulation parameter values was preliminarily determined through actual tests. Using the seed repose angle as the test index, the simulation parameters of the seed were calibrated through the Plackett—Burman test, the steepest climb test, and the Box–Behnken test. The suspension velocity of the seed model was determined by the Fluent–EDEM coupling simulation test, and the reliability of the discrete element model of the edible sunflower seed was verified. The simulated test results showed that the seed repose angle obtained by the optimization test was 35.823°, which exhibited a relative error of 0.103% in comparison to the average values obtained from the actual test. The simulated suspension velocity of the seed was 6.98 m/s. with a deviation of 0.55 m/s from the average suspension velocity obtained through the actual test. The discrete element model of the edible sunflower seed is accurate and reliable, offering guidance for improving the design of machinery used for seeding and harvesting edible sunflowers. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery Design for Smart Farming)
►▼ Show Figures

Figure 1

22 pages, 9808 KiB  
Article
An Efficient Group Convolution and Feature Fusion Method for Weed Detection
by Chaowen Chen, Ying Zang, Jinkang Jiao, Daoqing Yan, Zhuorong Fan, Zijian Cui and Minghua Zhang
Agriculture 2025, 15(1), 37; https://doi.org/10.3390/agriculture15010037 - 27 Dec 2024
Viewed by 509
Abstract
Weed detection is a crucial step in achieving intelligent weeding for vegetables. Currently, research on vegetable weed detection technology is relatively limited, and existing detection methods still face challenges due to complex natural conditions, resulting in low detection accuracy and efficiency. This paper [...] Read more.
Weed detection is a crucial step in achieving intelligent weeding for vegetables. Currently, research on vegetable weed detection technology is relatively limited, and existing detection methods still face challenges due to complex natural conditions, resulting in low detection accuracy and efficiency. This paper proposes the YOLOv8-EGC-Fusion (YEF) model, an enhancement based on the YOLOv8 model, to address these challenges. This model introduces plug-and-play modules: (1) The Efficient Group Convolution (EGC) module leverages convolution kernels of various sizes combined with group convolution techniques to significantly reduce computational cost. Integrating this EGC module with the C2f module creates the C2f-EGC module, strengthening the model’s capacity to grasp local contextual information. (2) The Group Context Anchor Attention (GCAA) module strengthens the model’s capacity to capture long-range contextual information, contributing to improved feature comprehension. (3) The GCAA-Fusion module effectively merges multi-scale features, addressing shallow feature loss and preserving critical information. Leveraging GCAA-Fusion and PAFPN, we developed an Adaptive Feature Fusion (AFF) feature pyramid structure that amplifies the model’s feature extraction capabilities. To ensure effective evaluation, we collected a diverse dataset of weed images from various vegetable fields. A series of comparative experiments was conducted to verify the detection effectiveness of the YEF model. The results show that the YEF model outperforms the original YOLOv8 model, Faster R-CNN, RetinaNet, TOOD, RTMDet, and YOLOv5 in detection performance. The detection metrics achieved by the YEF model are as follows: precision of 0.904, recall of 0.88, F1 score of 0.891, and mAP0.5 of 0.929. In conclusion, the YEF model demonstrates high detection accuracy for vegetable and weed identification, meeting the requirements for precise detection. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery Design for Smart Farming)
►▼ Show Figures

Figure 1

16 pages, 5432 KiB  
Article
Agricultural Machinery Movement Trajectory Recognition Method Based on Two-Stage Joint Clustering
by Shuya Zhang, Hui Liu, Xiangchen Cao and Zhijun Meng
Agriculture 2024, 14(12), 2294; https://doi.org/10.3390/agriculture14122294 - 14 Dec 2024
Viewed by 583
Abstract
To address the challenges posed by the large scale of agricultural machinery trajectory data and the complexity of actual movement trajectories, this paper proposes a two-stage joint clustering method for agricultural machinery trajectory recognition to enhance accuracy and robustness. The first stage involves [...] Read more.
To address the challenges posed by the large scale of agricultural machinery trajectory data and the complexity of actual movement trajectories, this paper proposes a two-stage joint clustering method for agricultural machinery trajectory recognition to enhance accuracy and robustness. The first stage involves trajectory clustering, where the spatial distribution characteristics of agricultural machinery trajectories are analyzed, and the position coordinates and the number of neighboring points of trajectory points are extracted as features. The silhouette coefficient method is used to determine the optimal number of clusters k for the K-Means algorithm, thus reducing the data scale. The second stage focuses on trajectory recognition, where a list of Eps and Minpts parameters is generated based on the statistical properties of the trajectory dataset. The Genetic Algorithm is employed for parameter optimization to determine the optimal DBSCAN parameters, enabling precise identification of field operation trajectories and road travel trajectories. Experimental results show that this method achieves mean values of 91.55% for Accuracy, 95.41% for Precision, 89.86% for Recall, and 92.41% for F1-score on a sample dataset of 337 trajectories, representing improvements of 12.8%, 5.13%, 7.79%, and 6.84%, respectively, over the traditional DBSCAN algorithm. Additionally, the Runtime of the two-stage joint clustering method is approximately 30% shorter than that of single-stage clustering. Compared with mainstream deep learning models such as LSTM and Transformer, this method delivers comparable recognition accuracy without the need for labeled data training, significantly reducing recognition costs. The proposed method achieves accurate and robust recognition of agricultural machinery trajectories and holds broad application potential in practical scenarios. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery Design for Smart Farming)
►▼ Show Figures

Figure 1

16 pages, 5667 KiB  
Article
Header Height Detection and Terrain-Adaptive Control Strategy Using Area Array LiDAR
by Chao Zhang, Qingling Li, Shaobo Ye, Jianlong Zhang and Decong Zheng
Agriculture 2024, 14(8), 1293; https://doi.org/10.3390/agriculture14081293 - 5 Aug 2024
Viewed by 896
Abstract
During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is [...] Read more.
During the operation of combine harvesters, the cutting platform height is typically controlled using manual valve hydraulic systems, which can result in issues such as delays in adjustment and high labor intensity, affecting both the quality and efficiency of the operation. There is an urgent need to enhance the automation level. Conventional methods frequently employ single-point measurements and lack extensive area coverage, which means their results do not fully represent the terrain’s variations in the area and are prone to local anomalies. Given the inherently undulating terrain of farmland during harvesting, a control strategy that does not adjust for minor undulations but only for significant ones proves to be more rational. To this end, a sine wave superposition model was established to simulate three-dimensional ground elevation changes, and an area array LiDAR was used to collect 8 × 8 data for the header height. The effects of mounds and stubble on the measurement results were analyzed, and a dynamic process simulation model for the solenoid valve core was developed to analyze the on/off delay characteristics of a three-position four-way electromagnetic directional valve. Moreover, a physical model of the hydraulic system was constructed based on the Simscape module in Simulink, and the Bang Bang switch predictive control system based on position threshold was introduced to achieve early switching of the electromagnetic directional valve circuit. In addition, an automatic control system for cutting platform height was designed based on an STM32 microcontroller. The control system was tested on the hydraulic automatic control test rig developed by Shanxi Agricultural University. The simulation and experimental results demonstrated that the control system and strategy were robust to output disturbances, effectively enhancing the intelligence and environmental adaptability of agricultural machinery operations. Full article
(This article belongs to the Special Issue Intelligent Agricultural Machinery Design for Smart Farming)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop