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Agriculture
Special Issues
Mathematical Modeling for Technological Processes of Agricultural Products
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Mathematical Modeling for Technological Processes of Agricultural Products

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

Deadline for manuscript submissions: 15 March 2025 | Viewed by 1077

Special Issue Editors

Dr. Yanlong Han

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: grain; mathematical modeling; simulation; agricultural products; mechanical structure
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Liu

E-Mail Website
Guest Editor
Modern Agricultural Engineering Key Laboratory at Universities of Education Department of Xinjiang Uygur Autonomous Region, Tarim University, Alaer 843300, China
Interests: sustainable agriculture; fruit quality; non-destructive detection; machine learning
Prof. Dr. Anqi Li

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: rice processing; agricultural product modeling; material analysis; parameter optimization; simulation design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The processing of agricultural products is of great significance in ensuring food security and promoting economic development. However, in actual agricultural production, there are still several problems such as high wastage rates, difficulty in quality control, and high energy consumption. An in-depth understanding of the working principles of the technological processes of agricultural products can help to solve these problems, but it is extremely difficult to monitor data from the production process. In recent years, with the advancement of computer technology, mathematical modeling has become an important method of solving such problems. Using mathematical modeling methods can provide an in-depth understanding of the working principles of processing of agricultural products; predict the impact of different process parameters on the quality of the product; reduce the consumption of energy and resources; and improve production efficiency.

This Special Issue focuses on the research of key variables and process parameters in the technological processes of agricultural products, calibration and validation of basic parameters of agricultural materials, and the establishment of mathematical models by combining the mechanism analysis method and the data-driven method, ultimately applying them to production practice. This Special Issue on “Mathematical Modeling for Technological Processes of Agricultural Products” will include interdisciplinary studies embracing agriculture with disciplines of biology and engineering. All types of articles, such as original research, opinions, and reviews, are welcome.

Dr. Yanlong Han
Dr. Yang Liu
Prof. Dr. Anqi Li
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. 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

  • processing of agricultural products
  • mathematical model
  • predictive model
  • parameter calibration
  • numerical simulation
  • mechanism analysis

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Published Papers (1 paper)

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Research

15 pages, 4674 KiB  
Article
Research on Automatic Alignment for Corn Harvesting Based on Euclidean Clustering and K-Means Clustering
by Bin Zhang, Hao Xu, Kunpeng Tian, Jicheng Huang, Fanting Kong, Senlin Mu, Teng Wu, Zhongqiu Mu, Xingsong Wang and Deqiang Zhou
Agriculture 2024, 14(11), 2071; https://doi.org/10.3390/agriculture14112071 - 18 Nov 2024
Viewed by 587
Abstract
Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned [...] Read more.
Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%. Full article
(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)
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