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Agriculture
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Grain Harvesting, Processing Technology and Storage Management—2nd Edition
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Grain Harvesting, Processing Technology and Storage Management—2nd Edition

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

Deadline for manuscript submissions: 20 December 2025 | Viewed by 539

Special Issue Editor

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
Special Issues, Collections and Topics in MDPI journals

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.

This Special Issue welcomes original research articles and reviews. 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
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

  • 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.)

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Published Papers (1 paper)

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Research

21 pages, 3121 KB  
Article
An Interpretable Stacked Ensemble Learning Framework for Wheat Storage Quality Prediction
by Xinze Li, Wenyue Wang, Bing Pan, Siyu Zhu, Junhui Zhang, Yunzhao Ma, Hongpeng Guo, Zhe Liu, Wenfu Wu and Yan Xu
Agriculture 2025, 15(17), 1844; https://doi.org/10.3390/agriculture15171844 - 29 Aug 2025
Viewed by 433
Abstract
Accurate prediction of wheat storage quality is essential for ensuring storage safety and providing early warnings of quality deterioration. However, existing methods focus solely on storage environmental conditions, neglecting the spatial distribution of temperature within grain piles, lacking interpretability, and generally failing to [...] Read more.
Accurate prediction of wheat storage quality is essential for ensuring storage safety and providing early warnings of quality deterioration. However, existing methods focus solely on storage environmental conditions, neglecting the spatial distribution of temperature within grain piles, lacking interpretability, and generally failing to provide reliable forecasts of future quality changes. To overcome these challenges, an interpretable prediction framework for wheat storage quality based on stacked ensemble learning is proposed. Three key features, Effective Accumulated Temperature (EAT), Cumulative High Temperature Deviation (CHTD), and Cumulative Temperature Gradient (CTG), were derived from grain temperature data to capture the spatiotemporal dynamics of the internal temperature field. These features were then input into the stacked ensemble learning model to accurately predict historical quality changes. In addition, future grain temperatures were predicted with high precision using a Graph Convolutional Network-Temporal Fusion Transformer (GCN-TFT) model. The temperature prediction results were then employed to construct features and were fed into the stacked ensemble learning model to enable future quality change prediction. Baseline experiments indicated that the stacked model significantly outperformed individual models, achieving R2 = 0.94, MAE = 0.44 mg KOH/100 g, and RMSE = 0.59 mg KOH/100 g. SHAP interpretability analysis revealed that EAT constituted the primary driver of wheat quality deterioration, followed by CHTD and CTG. Moreover, in future quality prediction experiments, the GCN-TFT model demonstrated high accuracy in 60-day grain temperature forecasts, and although the prediction accuracy of fatty acid value changes based on features derived from predicted temperatures slightly declined compared to features based on actual temperature data, it remained within an acceptable precision range, achieving an MAE of 0.28 mg KOH/100 g and an RMSE of 0.33 mg KOH/100 g. The experiments validated that the overall technical route from grain temperature prediction to quality prediction exhibited good accuracy and feasibility, providing an efficient, stable, and interpretable quality monitoring and early warning tool for grain storage management, which assists managers in making scientific decisions and interventions to ensure storage safety. Full article
(This article belongs to the Special Issue Grain Harvesting, Processing Technology and Storage Management—2nd Edition)
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