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AgriEngineering
Special Issues
Innovative Technologies for Agricultural Product Pre-Processing and Processing Engineering
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Innovative Technologies for Agricultural Product Pre-Processing and Processing Engineering

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Pre and Post-Harvest Engineering in Agriculture".

Deadline for manuscript submissions: 17 July 2026 | Viewed by 3848

Editor

Prof. Dr. Gabriel Henrique Horta de Oliveira

E-Mail Website
Guest Editor
Laboratory of Post-Harvest and Quality of Vegetable Products, Federal Institute of Southeast Minas Gerais, Manhuaçu, Brazil
Interests: post-harvest; drying; storage; physical properties; thermodynamic properties; fruits; grains; vegetables; rheological properties; thermic properties
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global population is growing, and the global climate is changing. These developments are key challenges that food production must overcome. To do so, innovative technologies for agricultural product pre-processing, processing, and storage engineering are required to increase productivity while using lower resources, thus contributing to a more sustainable supply chain. The aim of this Special Issue is to provide and discuss new technologies that impact agricultural product processing, from harvest to storage, aiming to increase the sustainability of the food supply chain. These technologies should valorize the resources used to produce foodstuff and any alterations within industry procedures, such as packaging, food transportation, and storage facilities.

Original research articles and reviews are welcome, and key topics of interest for publication include, but are not limited to, the following:

  • Artificial intelligence applied in agricultural product pre-processing, processing, and storage engineering;
  • Innovative post-harvest processes;
  • Solar drying of fruits, grains, and vegetables;
  • Mechanical drying of fruits, grains, and vegetables;
  • Storage facilities for fruits, grains, and vegetables;
  • Physical properties of food;
  • Drying and sorption thermodynamic properties of fruits, grains, and vegetables;
  • Rheological properties of foods;
  • Thermic properties of foods;
  • Seeds and seedlings production.

Prof. Dr. Gabriel Henrique Horta de Oliveira
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering 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 1800 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

  • post-harvest
  • drying
  • storage
  • fruits
  • grains
  • vegetables
  • quality

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

20 pages, 14463 KB  
Article
Pre-Sowing Treatment of Soybean Seeds in a High-Voltage DC and AC Electric Field
by Igor V. Yudaev and Yuliia V. Daus
AgriEngineering 2026, 8(6), 218; https://doi.org/10.3390/agriengineering8060218 - 31 May 2026
Viewed by 200
Abstract
Soybean (Glycine max L.) is a globally strategic crop valued for its high-quality protein and oil, yet its yield potential is frequently constrained by inconsistent seed germination and a heavy reliance on chemical treatments that carry environmental and health risks. Physical pre-sowing [...] Read more.
Soybean (Glycine max L.) is a globally strategic crop valued for its high-quality protein and oil, yet its yield potential is frequently constrained by inconsistent seed germination and a heavy reliance on chemical treatments that carry environmental and health risks. Physical pre-sowing stimulation has emerged as an eco-friendly alternative, but the comparative efficacy of direct current (DC) versus alternating current (AC) high-voltage electric fields—and the mechanistic basis for their differential effects—has remained poorly understood. Here, we systematically compared DC and AC pre-sowing treatments across a comprehensive matrix of field intensities (0.5, 1.0, and 1.5 kV/cm) and exposure durations (30, 60, and 120 s) at a fixed electrode gap of 10 cm, using soybean seeds of the Volgogradka 1 cultivar. Germination energy (day 3) and total germination (day 7) were assessed under standardized laboratory conditions in triplicate, followed by a replicated field trial to evaluate plant height, bean yield, and disease incidence. DC treatment significantly outperformed both the untreated control and AC treatment: germination energy increased by up to 60%, and total germination reached 100% compared with 85% in the control. The optimal DC window was identified at 0.8–1.5 kV/cm with a 30 s exposure. In stark contrast, AC treatment at industrial frequency not only failed to enhance germination but also frequently suppressed it and markedly increased susceptibility to fungal crown rot. Field results corroborated these findings: DC-treated seeds produced the highest bean mass (85 g per five plants vs. 80 g in the control), while AC-treated seeds yielded the lowest (72 g). Backward elimination regression analysis revealed that field intensity alone was the sole significant predictor of treatment outcomes, whereas exposure time and interaction effects were non-significant. We conclude that short-duration DC pre-sowing stimulation (1.0 kV/cm, 30–60 s) is a robust, chemically safe, and readily scalable technique for enhancing soybean establishment and yield. Conversely, AC treatment at power frequency is not recommended due to its deleterious effects on plant health and productivity. These findings establish a clear, evidence-based framework for the rational design of electrical seed treatment protocols. Full article
(This article belongs to the Special Issue Innovative Technologies for Agricultural Product Pre-Processing and Processing Engineering)
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20 pages, 3563 KB  
Article
Development of a Novel Walnut Sampling System and Rapid Moisture Measurement Methodology for a Commercial Walnut Hulling Facility
by Jaya Shankar Tumuluru, Paul A. Funk, Ronald P. Haff, Andrew Paul Breksa III, Joseph S. McIntyre, Kathleen M. Yeater, Derek P. Whitelock, Carlos B. Armijo, Yuzhu Zhang and Wally Yokoyama
AgriEngineering 2026, 8(4), 121; https://doi.org/10.3390/agriengineering8040121 - 30 Mar 2026
Viewed by 684
Abstract
Research is needed to improve walnut drying throughput and energy consumption in hulling plants, but current methods for sampling nuts in commercial drying bins and measuring nut moisture content limit the capacity to investigate the drying process thoroughly. A novel apparatus for obtaining [...] Read more.
Research is needed to improve walnut drying throughput and energy consumption in hulling plants, but current methods for sampling nuts in commercial drying bins and measuring nut moisture content limit the capacity to investigate the drying process thoroughly. A novel apparatus for obtaining walnut samples at multiple depths and locations in stadium drying bins and a novel rapid method for accurately determining walnut in-shell moisture content were developed. A second rapid moisture measurement method involving near-infrared light (NIR) was also investigated. The sampling apparatus consisted of three sampling columns installed in each walnut drying bin. Each column had gate valves at four elevations, admitting approximately 30 in-shell walnuts to rectangular buckets hanging on a cable just below each gate valve. To collect samples, the gates were opened and closed, the buckets were withdrawn, the nut samples were collected and sealed in labeled bags, and then the buckets were returned to the column to be ready for the next sampling interval. This configuration, sampling nuts at four levels across three locations in the drying bin, allowed better moisture content variability investigation during in-bin walnut drying. The rapid moisture content measurement method consisted of selecting twelve representative in-shell walnuts from each sample and grinding them in a mill. Twelve grams were sub-sampled from the well-mixed ground material and dried in an oven at 105 ± 1 °C for 3 h, then reweighed to determine moisture loss. The coefficient of variation for sub-samples within an individual sample (n = 4) averaged 2.65% for moisture contents ranging from 6% to 47% dry basis. The rapid moisture content measurement method reduced the drying time from 24 h to 3 h compared to conventional oven drying method, with an accuracy of ±0.5 to 1.5% of the full moisture content range. The best correlation observed between the NIR methodology and the rapid moisture content method was 0.74 R2. These new in-bin walnut sampling and moisture-content measurement methods will accelerate future research aimed at improving walnut drying at commercial huller facilities. Full article
(This article belongs to the Special Issue Innovative Technologies for Agricultural Product Pre-Processing and Processing Engineering)
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16 pages, 1686 KB  
Article
Optimized RT-DETRv2 Deep Learning Model for Automated Assessment of Tartary Buckwheat Germination and Pretreatment Evaluation
by Jian-De Lin, Chih-Hsin Chung, Hsiang-Yu Lai and Su-Der Chen
AgriEngineering 2025, 7(12), 414; https://doi.org/10.3390/agriengineering7120414 - 3 Dec 2025
Viewed by 907
Abstract
This study presents an optimized Real-Time Detection Transformer (RT-DETRv2) deep learning model for the automated assessment of Tartary buckwheat germination and evaluates the influence of soaking and ultrasonic pretreatments on the germination ratio. Model optimization revealed that image chip size critically affected performance. [...] Read more.
This study presents an optimized Real-Time Detection Transformer (RT-DETRv2) deep learning model for the automated assessment of Tartary buckwheat germination and evaluates the influence of soaking and ultrasonic pretreatments on the germination ratio. Model optimization revealed that image chip size critically affected performance. The 512 × 512-pixel chip size was optimal, providing sufficient image context for detection and achieving a robust F1-score (0.9754 at 24 h, tested with a ResNet-101 backbone). In contrast, smaller chips (e.g., 128 × 128 pixels) caused severe performance degradation (24 h F1 = 0.3626 and 48 h F1 = 0.1211), which occurred because the 128 × 128 chip was too small to capture the entire object, particularly as the elongated and highly variable 48 h sprouts exceeded the chip dimensions. The optimized model, incorporating a ResNet-34 backbone, achieved a peak F1-score of 0.9958 for 24 h germination detection, demonstrating its robustness. The model was applied to assess germination dynamics, indicating that 24 h of treatment with 0.1% CaCl2 and ultrasound enhanced total polyphenol accumulation (6.42 mg GAE/g). These results demonstrate that RT-DETRv2 enables accurate and efficient automated germination monitoring, providing a promising AI-assisted tool for seed quality evaluation and the optimization of agricultural pretreatments. Full article
(This article belongs to the Special Issue Innovative Technologies for Agricultural Product Pre-Processing and Processing Engineering)
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20 pages, 1446 KB  
Article
Design Thinking for the Development of an Affordable Pea Sheller: Addressing Co-Design in Rural Areas
by Ivonne Angelica Castiblanco Jimenez and Joan Paola Cruz Gonzalez
AgriEngineering 2025, 7(11), 360; https://doi.org/10.3390/agriengineering7110360 - 1 Nov 2025
Viewed by 1280
Abstract
Manual pea shelling is a labor-intensive task facing small-scale farmers in rural areas, requiring substantial physical effort and limiting productivity. This study employed a Design Thinking methodology to co-design an affordable, automatic pea sheller addressing the specific needs of resource-constrained farmers. The methodology [...] Read more.
Manual pea shelling is a labor-intensive task facing small-scale farmers in rural areas, requiring substantial physical effort and limiting productivity. This study employed a Design Thinking methodology to co-design an affordable, automatic pea sheller addressing the specific needs of resource-constrained farmers. The methodology comprised five phases: empathizing with farmers through interviews, defining technical specifications from user requirements and benchmarking analysis, ideating preliminary concepts through collaborative brainstorming, prototyping using 3D-printed food-grade materials, and testing with end-users under real operating conditions. The developed sheller features counter-rotating rollers operating at optimized speed with dual compartments for grain and shell separation. Experimental validation demonstrated good extraction efficiency with minimal grain damage, while field testing confirmed substantial time reduction compared to manual shelling and strong user acceptance. The fully 3D-printable design enables affordable, customizable production suitable for small-scale operations, demonstrating how user-centered co-design can create accessible agricultural technology that addresses both technical performance and socioeconomic constraints in rural communities. Full article
(This article belongs to the Special Issue Innovative Technologies for Agricultural Product Pre-Processing and Processing Engineering)
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