Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.6
3.6
Journals
Agriculture
Special Issues
Feature Papers in Agriculture Technology—Using Computer Simulation for Agricultural Machinery...
share announcement

Feature Papers in Agriculture Technology—Using Computer Simulation for Agricultural Machinery Design and Development

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 2450

Special Issue Editor

Dr. Mustafa Ucgul

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia
Interests: discrete element method (DEM); agricultural machinery design; tillage; soil mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the ever-changing world of agriculture, marked by the constant need to produce more food while managing resources and sustainability, the creation of better farming equipment plays a crucial role. Embracing technology and innovation is key to tackling these challenges, and computer simulation stands out as a powerful tool in this quest.

This Special Issue, titled "Using Computer Simulation for Agricultural Machinery Design and Development", explores the broad landscape of applying computer simulation techniques in agriculture. We are inviting researchers, engineers, and practitioners to contribute their knowledge and experience in this dynamic field.

We welcome original research articles and comprehensive reviews covering various topics, including advanced modelling and simulation (DEM, FEM, CFD, SPH), performance optimization, structural analysis, energy efficiency, human–machine interaction, and validation and verification.

This Special Issue is not just a collection of papers; it is a driving force in pushing the boundaries of agricultural machinery design and development through computer simulation.

Dr. Mustafa Ucgul
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 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

  • DEM
  • FEM
  • SPH
  • CFD
  • computer simulation
  • agricultural machinery

Published Papers (2 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, 8997 KiB  
Article
Parametric Design and Genetic Algorithm Optimization of a Natural Light Stereoscopic Cultivation Frame
by Dongdong Jia, Wengang Zheng, Xiaoming Wei, Wenzhong Guo, Qian Zhao and Guohua Gao
Agriculture 2024, 14(1), 84; https://doi.org/10.3390/agriculture14010084 - 30 Dec 2023
Viewed by 1096
Abstract
Vertical farming (VF) is an emerging cultivation frame that maximizes total plant production. However, the high energy-consuming artificial light sources for plants growing in the lower and middle layers significantly affect the sustainability of the current VF systems. To address the challenges of [...] Read more.
Vertical farming (VF) is an emerging cultivation frame that maximizes total plant production. However, the high energy-consuming artificial light sources for plants growing in the lower and middle layers significantly affect the sustainability of the current VF systems. To address the challenges of supplementary lighting energy consumption, this study explored and optimized the structural design of cultivation frames in VF using parametric modeling, a light simulation platform, and a genetic algorithm. The optimal structure was stereoscopic, including four groups of cultivation trough units in the lower layer, two groups in the middle layer, and one group in the upper layer, with a layer height of 685 mm and a spacing of 350 mm between the cultivation trough units. A field experiment demonstrated lettuce in the middle and lower layers yielded 82.9% to 92.6% in the upper layer. The proposed natural light stereoscopic cultivation frame (NLSCF) for VF was demonstrated to be feasible through simulations and on-site lettuce cultivation experiments without supplementary lighting. These findings confirmed that the NLSCF could effectively reduce the energy consumption of supplemental lighting with the ensure of lettuce’s regular growth. Moreover, the designing processes of the cultivation frame may elucidate further research on the enhancement of the sustainability and efficiency of VF systems. Full article
(This article belongs to the Special Issue Feature Papers in Agriculture Technology—Using Computer Simulation for Agricultural Machinery Design and Development)
►▼ Show Figures

Figure 1

17 pages, 18488 KiB  
Article
Detection of the Corn Kernel Breakage Rate Based on an Improved Mask Region-Based Convolutional Neural Network
by Hongmei Zhang, Zhijie Li, Zishang Yang, Chenhui Zhu, Yinhai Ding, Pengchang Li and Xun He
Agriculture 2023, 13(12), 2257; https://doi.org/10.3390/agriculture13122257 - 10 Dec 2023
Cited by 1 | Viewed by 784
Abstract
Real-time knowledge of kernel breakage during corn harvesting plays a significant role in the adjustment of operational parameters of corn kernel harvesters. (1) Transfer learning by initializing the DenseNet121 network with pre-trained weights for training and validating a dataset of corn kernels was [...] Read more.
Real-time knowledge of kernel breakage during corn harvesting plays a significant role in the adjustment of operational parameters of corn kernel harvesters. (1) Transfer learning by initializing the DenseNet121 network with pre-trained weights for training and validating a dataset of corn kernels was adopted. Additionally, the feature extraction capability of DenseNet121 was improved by incorporating the attention mechanism of a Convolutional Block Attention Module (CBAM) and a Feature Pyramid Network (FPN) structure. (2) The quality of intact and broken corn kernels and their pixels were found to be coupled, and a linear regression model was established using the least squares method. The results of the test showed that: (1) The MAPb50 and MAPm50 of the improved Mask Region-based Convolutional Neural Network (RCNN) model were 97.62% and 98.70%, in comparison to the original Mask Region-based Convolutional Neural Network (RCNN) model, which were improved by 0.34% and 0.37%, respectively; the backbone FLOPs and Params were 3.09 GMac and 9.31 M, and the feature extraction time was 206 ms; compared to the original backbone, these were reduced by 3.87 GMac and 17.32 M, respectively. The training of the obtained prediction weights for the detection of a picture of the corn kernel took 76 ms, so compared to the Mask RCNN model, it was reduced by 375 ms; based on the concept of transfer learning, the improved Mask RCNN model converged twice as quickly with the loss function using pre-training weights than the loss function without pre-training weights during training. (2) The coefficients of determination R2 of the two models, when the regression models of the pixels and the quality of intact and broken corn kernels were analyzed, were 0.958 and 0.992, respectively. These findings indicate a strong correlation between the pixel characteristics and the quality of corn kernels. The improved Mask RCNN model was used to segment mask pixels to calculate the corn kernel breakage rate. The verified error between the machine vision and the real breakage rate ranged from −0.72% to 0.65%, and the detection time of the corn kernel breakage rate was only 76 ms, which could meet the requirements for real-time detection. According to the test results, the improved Mask RCNN method had the advantages of a fast detection speed and high accuracy, and can be used as a data basis for adjusting the operation parameters of corn kernel harvesters. Full article
(This article belongs to the Special Issue Feature Papers in Agriculture Technology—Using Computer Simulation for Agricultural Machinery Design and Development)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop