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AgriEngineering
Special Issues
Recent Trends and Advances in Agricultural Engineering
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Recent Trends and Advances in Agricultural Engineering

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: 1 May 2025 | Viewed by 5091

Special Issue Editors

Dr. Pankaj B. Pathare

E-Mail Website1 Website2
Guest Editor
Department of Soils, Water and Agricultural Engineering, College of Agricultural and Marine Sciences, Sultan Qaboos University, Al-Khod 123, Oman
Interests: postharvest technology; drying; food quality; packaging; fresh produce transportation
Special Issues, Collections and Topics in MDPI journals
Dr. Peeyush Soni

E-Mail Website
Guest Editor
Indian Institute of Technology Kharagpur, Kharagpur 721302, India
Interests: sustainable agricultural mechanization; soil tillage and traction research; precision agriculture; management of mechanized agriculture; automation in agricultural operations

Special Issue Information

Dear Colleagues,

Agricultural engineering combines engineering expertise, a social conscience, and knowledge of living systems to address the complex issues facing our planet. To assure food security, reduce poverty, and promote sustainability, next-generation agriculture needs technological development; Agricultural engineering has the potential to significantly improve the sustainability of agriculture globally in all application areas, from the creation and effective usage of cutting-edge technology to the most recent implementation of digital farming solutions. It is directly associated with farm mechanization; automation and robotics; efficient irrigation systems; precision/conservation agriculture; farm energy systems; post-harvest storage/processing and value addition; remote sensing and geographical studies. A thorough understanding of the most modern agricultural equipment, technologies, and engineering solutions used in the farming processes is necessary to minimize any undesirable effects of field applications and increase crop output and sustainability. The objective of this special issue is to explore the current state and innovation in the agricultural and biosystemes engineeing and identify future trends and transformations, and, devoted exclusively to the publication of original papers, review articles and frontiers articles on biological and technological research in agriclture.

This Special Issue focuses, but is not limited, to the agricultural engineering in the following areas:

  • Smart agricultural machinery, equipment, and structures;
  • Soil tillage and traction;
  • Agricultural resource management for efficiency and sustainability;
  • Agro-food waste management;
  • Biofuels and renewable energies;
  • Computers, Electronics and ICT applications in agriculture;
  • AI/ML applications in agriculture;
  • Precision agriculture;
  • Proximal sensing and GIS in agriculture;
  • Automation in food and agriculture;
  • Postharvest Technology & Management;
  • Food handling & storage;
  • Advances in Agro-processing
  • Water management 

Dr. Pankaj B. Pathare
Dr. Peeyush Soni
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. AgriEngineering is an international peer-reviewed open access quarterly 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

  • agricultural mechanization
  • postharvest
  • precision agriculture
  • imaging and machine learning
  • soil management
  • remote sensing
  • sensors in food safety
  • AI in agriculture

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

Published Papers (4 papers)

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Research

16 pages, 14254 KiB  
Article
Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction
by Merima Smajlhodžić-Deljo, Madžida Hundur Hiyari, Lejla Gurbeta Pokvić, Nejra Merdović, Faruk Bećirović, Lemana Spahić, Željana Grbović, Dimitrije Stefanović, Ivana Miličić and Oskar Marko
AgriEngineering 2024, 6(4), 4704-4719; https://doi.org/10.3390/agriengineering6040269 - 5 Dec 2024
Viewed by 548
Abstract
Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles [...] Read more.
Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution images captured by unmanned aerial vehicles (UAVs). Drone-based images were captured during the late growth stage of wheat across 15 fields in Bosnia and Herzegovina. The images, processed to a resolution of 1024 × 1024 pixels, were manually annotated with regions of interest (ROIs) containing wheat ears. A dataset consisting of 556 high-resolution images was compiled, and advanced models including Faster R-CNN, YOLOv8, and RT-DETR were utilised for ear detection. The study found that although lower-quality images had a minor effect on detection accuracy, they did not significantly hinder the overall performance of the models. This research demonstrates the potential of digital technologies, particularly machine learning and UAVs, in transforming traditional agricultural practices. The novel application of automated ear counting via machine learning provides a scalable, efficient solution for yield prediction, enhancing sustainability and competitiveness in agriculture. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Agricultural Engineering)
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14 pages, 2603 KiB  
Article
Optimization of the Design of a Greenhouse LED Luminaire with Immersion Cooling
by Pavel V. Tikhonov, Alexander A. Smirnov, Yuri A. Proshkin, Dmitry A. Burynin, Sergey A. Kachan and Alexey S. Dorokhov
AgriEngineering 2024, 6(3), 3460-3473; https://doi.org/10.3390/agriengineering6030197 - 19 Sep 2024
Viewed by 912
Abstract
Modern agriculture, with the use of artificial lighting, requires high-intensity LED luminaires with compact dimensions. In this regard, new approaches to the design of LED luminaires using both new materials and technical solutions have been considered. The theoretical evaluation of the influence of [...] Read more.
Modern agriculture, with the use of artificial lighting, requires high-intensity LED luminaires with compact dimensions. In this regard, new approaches to the design of LED luminaires using both new materials and technical solutions have been considered. The theoretical evaluation of the influence of different materials on the efficiency of removal of thermal energy from LEDs was shown. A new material PMS-5 is proposed and evaluated as an immersion liquid, which can be used as an effective heat sink in the lower part of the luminaire up to the level of LEDs located in the top light LED luminaires. The proposed polymethylsiloxane PMS-5 liquid has more than twice the thermal conductivity (0.167 W/(m·K)) of HFE7200 and NS15 liquids used in immersion-cooled LED luminaires. Based on the theoretical evaluation, the requirements for parameters, such as metal profile area, immersion liquid volume, wall thickness area, and external area of the cylinder, are highlighted and shown. The noted parameters have a key role in the design of an efficient top light LED luminaire. It has been shown that the design of the metal profile significantly affects the efficiency of the removal of thermal energy from LEDs and it is necessary to use specialized profiles optimized for the diameter of the LED luminaire cylinder. A number of LED luminaire designs were proposed, depending on the thermal properties of the construction materials, technical and economic performance, as well as actual operating and installation conditions. The analysis of the presented theoretical evaluations allowed overlay of the design basis of LED luminaires within the presented concept and top light. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Agricultural Engineering)
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18 pages, 7892 KiB  
Article
GamaNNet: A Novel Plant Pathologist-Level CNN Architecture for Intelligent Diagnosis
by Marcio Oliveira, Adunias Teixeira, Guilherme Barreto and Cristiano Lima
AgriEngineering 2024, 6(3), 2623-2639; https://doi.org/10.3390/agriengineering6030153 - 2 Aug 2024
Cited by 1 | Viewed by 780
Abstract
Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage [...] Read more.
Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage of Solanum lycopersicum (tomato). Ten thousand RGB images of leaf tissue were subsampled in training (64%), validation (16%), and test (20%) sets to rank the most suitable CNNs in expediting the diagnosis of plant disease. The study assessed the performance of eight well-known networks under identical hyperparameter conditions. Additionally, it introduced the GamaNNet architecture, a custom-designed model optimised for superior performance on this specific type of dataset. The investigational results were most promising for the innovative GamaNNet and ResNet-152, which both exhibited a 91% accuracy rate, as evidenced by their confusion matrices, ROC curves, and AUC metrics. In comparison, LeNet-5 and ResNet-50 demonstrated lower assertiveness, attaining accuracies of 74% and 69%, respectively. GoogLeNet and Inception-v3 emerged as the frontrunners, displaying diagnostic preeminence, achieving an average F1-score of 97%. Identifying such pathologies as Early Blight, Late Blight, Corynespora Leaf Spot, and Septoria Leaf Spot posed the most significant challenge for this class of problem. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Agricultural Engineering)
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14 pages, 3778 KiB  
Article
Plant Growth Regulator from the Essential Oil of Syzygium aromaticum L. for Inhibition of Secondary Growth of Garlic Cultivated under Tropical Conditions
by Vinícius Guimarães Nasser, Willian Rodrigues Macedo, Frederico Garcia Pinto, Junio Henrique da Silva, Marcelo Coelho Sekita and Geraldo Humberto Silva
AgriEngineering 2024, 6(2), 1511-1524; https://doi.org/10.3390/agriengineering6020086 - 29 May 2024
Cited by 1 | Viewed by 878
Abstract
Garlic cultivation in tropical regions, such as the Brazilian Cerrado, faces the problem of secondary growth in the field induced by climatic conditions, which affects bulb quality and value. Clove essential oil (CEO) contains high levels of eugenol, which has the potential as [...] Read more.
Garlic cultivation in tropical regions, such as the Brazilian Cerrado, faces the problem of secondary growth in the field induced by climatic conditions, which affects bulb quality and value. Clove essential oil (CEO) contains high levels of eugenol, which has the potential as an eco-friendly plant growth retardant (PGR) capable of reducing or inhibiting the secondary growth of bulbs in garlic cultivation. In this study, field experiments were carried out in two consecutive years (winter 2021 and 2022), spraying garlic plants with different concentrations of emulsion of CEO (0.0, 0.2, and 0.4%) in the differentiation phase; for comparison, the effects of water deficit, a prevalent agricultural technique in the region, were also evaluated. At a dose of 0.4%, the CEO reduced the prevalence of secondary growth and split bulbs without affecting yield. The mode of action of PGR was investigated by analyzing photosynthetic, enzymatic, and metabolomic parameters. The plants reduced amylolytic activity, and the photosynthetic parameters, after 7 days, were restored in both treatments. The analysis of the metabolomic profile of garlic leaves revealed changes in the pathways associated with the biosynthesis of fatty acids, wax, cutin, and suberin in plants treated with CEO, indicating possible damage to the surface coating of the leaf. Full article
(This article belongs to the Special Issue Recent Trends and Advances in Agricultural Engineering)
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