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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 982

Special Issue 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-blind 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 1600 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 (2 papers)

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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
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 551
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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