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Agriculture
Special Issues
Grain Harvesting, Processing Technology, and Storage Management
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Grain Harvesting, Processing Technology, and Storage Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Product Quality and Safety".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 5118

Special Issue Editors

Prof. Dr. Wenfu Wu

E-Mail Website
Guest Editor
College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Interests: food harvesting, storage, and processing technology; smart grain, smart farming, and smart grain systems; stored grain ecosystems; multi-field interaction and multi-factor coupling
Dr. Jun Yin

E-Mail Website
Guest Editor
Academy of State Administration of Grain (ASAG), Baiwanzhuang Street, Beijing 100037, China
Interests: theoretical models of moisture and heat transfer in grain; control models for grain drying

Special Issue Information

Dear Colleagues,

In the context of an evolving global food system and growing concerns regarding food security and quality, it is crucial to explore innovative approaches and advancements across various aspects of the food system. Effective management and technological innovation in grain collection, storage, transportation, and processing, along with the integration of food safety information systems, smart grain systems, and artificial intelligence technologies, play a vital role in enhancing grain quality, reducing losses, and ensuring the sustainability and safety of the grain supply chain.

We are delighted to invite scholarly contributions that investigate the interlinkages between food security, food quality, and food security management information systems. Additionally, we encourage the submission of research on advancements in food production and processing technology, as well as on grain storage and management. Furthermore, we are particularly interested in exploring multi-field interactions and multi-factor coupling within the context of grain harvesting, postharvest control, and the eco-concept of stored grain. We highly encourage contributions that delve into smart grain systems, artificial intelligence techniques, and information technology.

This Special Issue aims to delve into the advancements, challenges, and opportunities pertaining to various aspects of the food system, including food harvesting, storage, transportation, and processing, with a strong focus on improving efficiency, sustainability, and overall management.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: grain harvesting; processing technology; grain storage; smart grain and smart grain systems; artificial intelligence; and information technology.

Prof. Dr. Wenfu Wu
Dr. Jun Yin
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 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

  • food quality and security
  • food production and processing technology
  • grain harvesting, postharvest control, and harvest losses
  • grain storage and management
  • grain condition and detection system
  • stored grain ecosystems and eco-concept of stored grain
  • multi-field interaction and multi-factor coupling (abiotic and biotic constituents)
  • smart grain, smart farming, and smart grain systems
  • artificial intelligence (numerical simulation, machine deep learning, expert systems, artificial neural networks, etc.)
  • information technology (digital twin, big data, database management, software development and applications, cloud computing and virtualization, etc.)

Published Papers (4 papers)

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Research

Jump to: Review

11 pages, 1714 KiB  
Article
Correlation Analysis of Sitophilus oryzae (Linnaeus) Real-Time Monitoring and Insect Population Density and Its Distribution Pattern in Wheat Grain Piles
by Zeyu Zhang, Guoxin Zhou, Cui Miao, Xin Du and Zhongming Wang
Agriculture 2024, 14(8), 1327; https://doi.org/10.3390/agriculture14081327 (registering DOI) - 9 Aug 2024
Viewed by 237
Abstract
The traditional manual sampling method for detecting stored grain insect pests is labor-intensive and time-consuming, often yielding non-representative samples. However, to achieve more accurate monitoring, it is necessary to understand the distribution patterns of different insect pests within grain silo and their correlation [...] Read more.
The traditional manual sampling method for detecting stored grain insect pests is labor-intensive and time-consuming, often yielding non-representative samples. However, to achieve more accurate monitoring, it is necessary to understand the distribution patterns of different insect pests within grain silo and their correlation with monitoring and sampling data. This study aimed to assess the population density and distribution of Sitophilus oryzae (rice weevil) in bulk wheat grain to predict insect dynamics effectively. Utilizing a probe trap in a wheat silo, adult insects were tracked across different population densities. The traps recorded captured pests, alongside temperature and humidity data. The correlation analysis revealed that rice weevils were active throughout the silo but less prevalent at the bottom, with the highest distribution near the upper surface. Temperature and humidity significantly influenced their activity, particularly within the 22 °C to 32 °C range. Higher population densities correlated with increased relative humidity, impacting weevil activity. Trapping data aligned with overall population density changes in the silo. This study will provide an accurate assessment of the population density of adult rice weevils in grain silos based on temperature changes in the upper part of the grain silo. Full article
(This article belongs to the Special Issue Grain Harvesting, Processing Technology, and Storage Management)
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Figure 1

Figure 1
<p>A real-time insect trap device based on photoelectric infrared sensors, which were jointly developed by Beijing University of Posts and Telecommunications and the Scientific Research Institute of the State Administration of Grain and Material Reserves.</p> Full article ">Figure 2
<p>Three-tonne capacity experimental silo (<b>a</b>), a schematic diagram of the silo size (<b>b</b>) and trap locations (<b>b</b>,<b>c</b>). OD = Outside Diameter; T = Top; M = Middle; B = Bottom; No. 1, 2, 3, 4 and 5 represent five entrapment points. Red colored cylinders represent probe traps.</p> Full article ">Figure 3
<p>Time series chart of the daily catch of 15 traps in three repeated experiments at 0.1 insect/kg, 1.0 insect/kg, and 5.0 insect/kg. R1, R2, and R3 represent the three repeated experiments that were conducted.</p> Full article ">Figure 3 Cont.
<p>Time series chart of the daily catch of 15 traps in three repeated experiments at 0.1 insect/kg, 1.0 insect/kg, and 5.0 insect/kg. R1, R2, and R3 represent the three repeated experiments that were conducted.</p> Full article ">Figure 4
<p>Three-dimensional scatter plots of daily average rice temperature, relative humidity, and trap capture quantity per probe at three different pest population densities: 0.1 (<b>a</b>), 1.0 (<b>b</b>), and 5.0 (<b>c</b>) adults/kg.</p> Full article ">
16 pages, 2140 KiB  
Article
Desorption and Sorption Isotherms of Different Varieties of Hemp Seeds with Different Percentages of Dockage under Different Temperatures and Different Relative Humidities
by Abhinav Tiwari and Fuji Jian
Agriculture 2023, 13(10), 1959; https://doi.org/10.3390/agriculture13101959 - 8 Oct 2023
Viewed by 985
Abstract
Hemp cultivation faces challenges due to the adoption of dioecious cultivars, which suffer from biomass loss and fibre heterogeneity. In contrast, monoecious cultivars offer simultaneous fibre and seed production, albeit with lower fibre quality. Understanding the drying characteristics and storage requirements of hemp [...] Read more.
Hemp cultivation faces challenges due to the adoption of dioecious cultivars, which suffer from biomass loss and fibre heterogeneity. In contrast, monoecious cultivars offer simultaneous fibre and seed production, albeit with lower fibre quality. Understanding the drying characteristics and storage requirements of hemp seeds is crucial for effective post-harvest management. This study explored the moisture sorption and desorption isotherms of two common Canadian hemp seed varieties, Altair (dioecious) and CanMa (monoecious), by using both saturated salt solution (SSS) and thin-layer drying methods. Their isotherms were also compared with the published isotherm of Finola—a common dioecious variety in Europe. The thin-layer drying method yielded higher EMC values than the SSS method due to incomplete equilibrium attainment. Larger EMC differences existed between different seed types (dioecious vs. monoecious), and this difference was small between the same seed types (dioecious vs. dioecious). The GAB equation provided the most accurate prediction of equilibrium moisture contents for both varieties. Full article
(This article belongs to the Special Issue Grain Harvesting, Processing Technology, and Storage Management)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Isotherm of Altair with different dockage percentages (0, 5, 10, and 15%) under desorption and adsorption conditions and at different environmental conditions (temperature: 10, 20, 30, 35 °C; and RH from 34 to 94%) generated by salt solutions. In the graph, the line shows the value predicted by the developed GAB equation and different symbols show the measured values with different dockage percentages. In the legend, Des = desorption, and Sor = sorption.</p> Full article ">Figure 2
<p>Isotherm of CanMa with different dockage percentages (0, 5, 10, and 15%) under desorption and adsorption conditions and at different environmental conditions (temperature: 10, 20, 30, 35 °C; and RH from 34 to 94%) generated by salt solutions. In the graph, the line shows the value predicted by the developed GAB equation and different symbols show the measured values with different dockage percentages. In the legend, Des = desorption, and Sor = sorption.</p> Full article ">Figure 3
<p>Isotherm of CanMa and Altair samples under thin layer drying conditions (temperature: 30, 35 and 40 °C, RH: 30, 50, 70%). In the graph, the line shows the values predicted by the developed GAB equation and different symbols show the measured equilibrium moisture contents.</p> Full article ">Figure 4
<p>Drying rate of CanMa and Altair hemp seeds under different drying temperatures, RHs, and drying times using the thin-layer drying method.</p> Full article ">
16 pages, 2296 KiB  
Article
The Effect of Drying Variables on the Microwave–Vacuum-Drying Characteristics of Mulberries (Morus alba L.): Experiments and Multivariate Models
by Yuyang Cong, Yang Liu, Yurong Tang, Jiale Ma, Xingyu Wang, Shuai Shen and Hong Zhang
Agriculture 2023, 13(9), 1843; https://doi.org/10.3390/agriculture13091843 - 20 Sep 2023
Viewed by 1253
Abstract
It is easy to cause increases in temperature and the gasification of water in materials, facilitated via supercharging and the generation of instantaneous strong pressure under the collaborative action of a microwave and a vacuum, thus facilitating the internal cell swelling of materials, [...] Read more.
It is easy to cause increases in temperature and the gasification of water in materials, facilitated via supercharging and the generation of instantaneous strong pressure under the collaborative action of a microwave and a vacuum, thus facilitating the internal cell swelling of materials, changes in fibre structures, and the formation of loose and uniform microstructures. In this experiment, mulberries were dehydrated using microwave–vacuum drying technology. The drying characteristics were disclosed by using crispness as the evaluation index and multiple drying parameters (e.g., products’ surface temperature, microwave power, chamber vacuum level and drying height) as the control variables. The optimised Two-term model can predict the dehydration process of mulberries under multiple drying variables, as determined through the experimental data. The optimal drying variables were determined according to the crispness of the dried mulberries. The optimal puffing quality of mulberries could be gained under a product surface temperature = 50 °C, microwave power = 5.45 W/g, a chamber vacuum level = 0.08 MPa and a drying height = 0 cm. The diffusion coefficient of the available water of the mulberries during the microwave–vacuum drying process ranges from 4.98 × 10−8 to 3.81 × 10−7, and the activation energy for drying is 183.923 KJ/mol. Full article
(This article belongs to the Special Issue Grain Harvesting, Processing Technology, and Storage Management)
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Figure 1

Figure 1
<p>Flow chart of microwave–vacuum drying test for mulberries. (1) PP material drying tray; (2) 100 ± 3 g fresh mulberries are placed evenly (3) in the microwave vacuum drying oven; (4) parameters of the microwave vacuum drying are set; (5) mulberries are dried; and (6) the moisture content is determined.</p> Full article ">Figure 2
<p>(<b>a</b>) The effect of product surface temperature on the brittleness of mulberry crisps; (<b>b</b>) the effect of microwave power on the brittleness of mulberry crisps; (<b>c</b>) the effect of chamber vacuum level on the brittleness of mulberry crisps; (<b>d</b>) the effect of drying position on the brittleness of mulberry crisps; (<b>e</b>) the effect of product surface temperature on the drying rate of mulberries; (<b>f</b>) the effect of microwave power on the drying rate of mulberries; (<b>g</b>) the effect of chamber vacuum level on the drying rate of mulberries; (<b>h</b>) the effect of drying position on the moisture content of mulberries.</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) The effect of product surface temperature on the brittleness of mulberry crisps; (<b>b</b>) the effect of microwave power on the brittleness of mulberry crisps; (<b>c</b>) the effect of chamber vacuum level on the brittleness of mulberry crisps; (<b>d</b>) the effect of drying position on the brittleness of mulberry crisps; (<b>e</b>) the effect of product surface temperature on the drying rate of mulberries; (<b>f</b>) the effect of microwave power on the drying rate of mulberries; (<b>g</b>) the effect of chamber vacuum level on the drying rate of mulberries; (<b>h</b>) the effect of drying position on the moisture content of mulberries.</p> Full article ">Figure 3
<p>Two-term model validation.</p> Full article ">

Review

Jump to: Research

13 pages, 1934 KiB  
Review
Progress and Prospective in the Development of Stored Grain Ecosystems in China: From Composition, Structure, and Smart Construction to Wisdom Methodology
by Yunshandan Wu, Wenfu Wu, Kai Chen, Ji Zhang, Zhe Liu and Yaqiu Zhang
Agriculture 2023, 13(9), 1724; https://doi.org/10.3390/agriculture13091724 - 31 Aug 2023
Cited by 1 | Viewed by 1943
Abstract
Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring [...] Read more.
Food security is intrinsically linked to maintaining optimal physical health and promoting active lifestyles. Stored Grain Ecosystems (SGEs) are complex systems comprising a range of grains, microorganisms, and environmental elements. To ensure sustainable grain storage and promote food-friendly SGEs, careful regulation and monitoring of these factors are vital. This review traces the evolution of the Eco-concept of stored grain in China, focusing on micro- and macro-structural composition, the Multi-field/Re-coupling structure, and Smart Construction of SGEs, while introducing the four development lines and Wisdom Methodology of SGEs. The current status and challenges of SGEs in China are also discussed. The Eco-concept of stored grain in China has progressed through the initial exploration period, formation and practice periods, and has now entered its fourth stage, marked by a shift to include interactions of multiple biological fields. This evolution extends beyond the traditional binary relationship and offers emerging technologies greater scope for scientific and intelligent theoretical analysis of grain storage practices. The Wisdom Methodology employs a multifaceted, Mechanism and Data-driven approach, incorporating four driving methods, and is now widely recognized as a leading strategy for researching Smart Grain Systems. Digital Twin technology enables precise simulations and mappings of real-world SGEs in a virtual environment, supporting accurate assessments and early warnings for issues concerning grain conditions. Driven by Mechanism and Data, Digital Twin solutions are a pioneering trend and emerging hotspot with vast potential for enhancing the intelligence and wisdom of future grain storage processes. Overall, this review provides valuable guidance to practitioners for advancing high-quality Smart Grain Systems, enhancing sustainable and intelligent grain storage practices. Full article
(This article belongs to the Special Issue Grain Harvesting, Processing Technology, and Storage Management)
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Figure 1

Figure 1
<p>Macrostructure of Stored Grain Ecosystem (<a href="#agriculture-13-01724-f001" class="html-fig">Figure 1</a> is adapted from Refs. [<a href="#B2-agriculture-13-01724" class="html-bibr">2</a>,<a href="#B14-agriculture-13-01724" class="html-bibr">14</a>]). The SGEs comprise the Grain Ecological Subsystem and the Environmental Ecological Subsystem, with factors in each subsystem impacting and intersecting to determine the overall stability of the systems.</p> Full article ">Figure 2
<p>Microstructure of Stored Grain Ecosystems [<a href="#B17-agriculture-13-01724" class="html-bibr">17</a>]. (<b>a</b>) Physical microstructure (CAE model); (<b>b</b>) internal microstructure.</p> Full article ">Figure 3
<p>Multi-field/Re-coupling relationship of Grain Storage Ecosystems [<a href="#B17-agriculture-13-01724" class="html-bibr">17</a>]. SGEs are characterized by a coupling relationship with both external and internal environments.</p> Full article ">Figure 4
<p>Smart Construction of Stored Grain Ecosystems [<a href="#B17-agriculture-13-01724" class="html-bibr">17</a>]. The technological breakthroughs have provided a continuous impetus for SGEs to shift towards a more diverse, extensive, scientifically based, and intelligent direction: (<b>a</b>) Physical-biological coupling; (<b>b</b>) physical-biological-HI coupling; (<b>c</b>) physical-biological-AI coupling.</p> Full article ">Figure 5
<p>Wisdom Methodology of Stored Grain Ecosystems [<a href="#B17-agriculture-13-01724" class="html-bibr">17</a>]. The Wisdom Methodology integrates four types of research methods to identify new approaches for solving complex problems within SGEs.</p> Full article ">
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