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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 September 2025 | Viewed by 2532

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 (4 papers)

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Research

17 pages, 5429 KiB  
Article
The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains
by Zhenwei Liang, Xingyue Xu, Deyong Yang and Yanbin Liu
Agriculture 2025, 15(8), 848; https://doi.org/10.3390/agriculture15080848 - 14 Apr 2025
Viewed by 195
Abstract
A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. This model achieves correct recognition, classification, and detection of rice target crops [...] Read more.
A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. This model achieves correct recognition, classification, and detection of rice target crops with similar colors in complex environments. Firstly, changing the CBS module to the DBS module in the entire network model and replacing the standard convolution with Depthwise Separable Convolution (DSConv) can effectively reduce the number of parameters and the computational complexity, making the model lightweight. The ECANet module is introduced into the backbone feature extraction network, utilizing the weighted selection feature to cluster the network in the region of interest, enhancing attention to rice impurities and broken grains, and compensating for the reduced accuracy caused by model light weighting. The loss problem of class imbalance is optimized using the Focal Loss function. The experimental results demonstrate that the DE-YOLO model has an average accuracy (mAP) of 97.55% for detecting rice impurity crushing targets, which is 2.9% higher than the average accuracy of the original YOLOX algorithm. The recall rate (R) is 94.46%, the F1 value is 0.96, the parameter count is reduced by 48.89%, and the GFLOPS is reduced by 46.33%. This lightweight model can effectively detect rice impurity/broken targets and provide technical support for monitoring the rice impurity/ broken rate. Full article
(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)
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16 pages, 5727 KiB  
Article
Numerical Analysis of Influence Mechanism of Orifice Eccentricity on Silo Discharge Rate
by Yinglong Wang, Yanlong Han, Anqi Li, Hao Li, Haonan Gao, Ze Sun, Shouyu Ji, Zhuozhuang Li and Fuguo Jia
Agriculture 2025, 15(5), 490; https://doi.org/10.3390/agriculture15050490 - 25 Feb 2025
Viewed by 354
Abstract
Eccentric silo is an extremely common type of silo, but it is still unclear how to accurately control the discharge by adjusting eccentric orifices, limiting the application and development of eccentric silo. In this study, the rice particle discharging process on silos with [...] Read more.
Eccentric silo is an extremely common type of silo, but it is still unclear how to accurately control the discharge by adjusting eccentric orifices, limiting the application and development of eccentric silo. In this study, the rice particle discharging process on silos with different eccentricities was simulated by the discrete element method (DEM), and the influence mechanism of orifice eccentricity on silo discharge rate was analyzed. The results show that eccentricity has a direct influence on the particle volume fraction and vertical velocity that determine the discharge rate of the silo. In fully eccentric silo, it is not easy for particle flow to achieve balance, particles will pass through outlet with more kinetic energy. Moreover, continuous force network cannot be formed between particles with shear resistance, resulting in weak interlocking action between particles. The orientation of particle in fully eccentric silo is more vertical, especially near the silo wall, which will produce larger local particle volume fraction above the orifice. When the eccentricity exceeds the critical eccentricity, the sparse flow area on the discharge orifice becomes larger, and the particle acceleration area increases accordingly. Research findings may offer valuable insights for the accurate control of discharge rate of eccentric silo, as well as for optimizing silo design. Full article
(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)
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17 pages, 14318 KiB  
Article
Development of a Mathematical Model and Structural Optimization of the Specific Resistance of a Broken Line Subsoiler
by Da Qiao, Qian Shi, Pinyan Lv, Yingjie Guo, Daping Fu, Min Liu, Limin Jiang, Yang Wang, Jingli Wang and Weizhi Feng
Agriculture 2025, 15(3), 352; https://doi.org/10.3390/agriculture15030352 - 6 Feb 2025
Viewed by 507
Abstract
Saline-alkali soil has the characteristics of high density, high firmness and poor permeability. Aiming at the problems of shallow subsoiling depth, large subsoiling specific resistance and small soil bulkiness in subsoiling operation in saline-alkali soil, this paper establishes a mathematical model of the [...] Read more.
Saline-alkali soil has the characteristics of high density, high firmness and poor permeability. Aiming at the problems of shallow subsoiling depth, large subsoiling specific resistance and small soil bulkiness in subsoiling operation in saline-alkali soil, this paper establishes a mathematical model of the specific resistance of a broken line subsoiler and uses genetic algorithm and the discrete element method to optimize the structure design of the subsoiler. Firstly, the mathematical model was developed by analyzing the force of the subsoiler in the working process. The genetic algorithm was used to solve the problem, and three geometric models of the broken line subsoilers were fitted. Then, EDEM software was used to simulate this, and the tillage performance was evaluated with draft force, soil disturbance area, subsoiling specific resistance and soil bulkiness as the indexes and verified by field experiment. The results showed that the subsoiling specific resistance of the three broken line subsoilers was significantly lower than that of the standard subsoiler in the simulation test. Compared to the standard subsoiler, the soil disturbance area of the broken line subsoiler-B increased by 12%, the draft force decreased by 19%, the subsoiling specific resistance decreased by 26% and the bulkiness increased by 6%. The field experiment results showed that the broken line subsoiler-B reduced the traction force and improved the tillage efficiency compared to the standard subsoiler, which was consistent with the analysis results of EDEM. The broken line subsoiler can effectively enhance the quality of cultivated land. Full article
(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)
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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 791
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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