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Applied Sciences
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Technologies and Techniques for the Enhancement of Agriculture 4.0
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Technologies and Techniques for the Enhancement of Agriculture 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2388

Special Issue Editors

Prof. Dr. Roberto Romaniello

E-Mail Website
Guest Editor
Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: agri-food engineering; agricultural machinery; image analysis; mechanical plants; food plants
Special Issues, Collections and Topics in MDPI journals
Dr. Claudio Perone

E-Mail Website
Guest Editor
Department of Agriculture, Food, Natural Resource and Engineering, University of Foggia, 71122 Foggia, Italy
Interests: agri-food engineering; energy savings; mechanical plants; heat transfer; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Greater improvements in Agriculture 4.0 technologies create new opportunities for farms of all sizes, additionally offering economic opportunities in terms of sustainability, which could further be advanced with agricultural operations being managed intelligently through technological innovation in sensors, devices, machines and information technology. It is crucial to increase the use of sophisticated technologies such as robots, sensors, hyperspectral imaging and positioning technologies. As such, agricultural companies, in addition to becoming more profitable, will be more efficient, safe and respectful of the environment. To implement such a technological transition at any level, a greater effort is needed to make such innovative and complex technologies accessible to all. In this context, it is necessary to strengthen research into and the development of models, devices and services, such as the following: proximal and remote sensing, data processing, digital technologies, informed decision making, telecommunications, AI and robotics.

Therefore, this Special Issue welcomes submissions of original research and review studies in the abovementioned research areas.

Prof. Dr. Roberto Romaniello
Dr. Claudio Perone
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. Applied Sciences 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 2400 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

  • Agriculture 4.0
  • smart farming
  • agri-technologies
  • precision agriculture
  • information technologies
  • robotics
  • machine learning
  • big data
  • artificial intelligence (AI)
  • Internet of Things (IoT)
  • virtual and augmented reality

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Published Papers (4 papers)

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Research

23 pages, 4923 KiB  
Article
Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
by Roberto Finistrosa, Carolina Mañoso, Ángel P. de Madrid and Miguel Romero
Appl. Sci. 2025, 15(9), 4856; https://doi.org/10.3390/app15094856 - 27 Apr 2025
Viewed by 313
Abstract
In medium- and large-scale poultry farms, automated systems optimize key processes, from egg production and grading to environmental control, reducing manual labor and ensuring an optimal environment for the birds. However, these technologies remain largely inaccessible to small family farms due to high [...] Read more.
In medium- and large-scale poultry farms, automated systems optimize key processes, from egg production and grading to environmental control, reducing manual labor and ensuring an optimal environment for the birds. However, these technologies remain largely inaccessible to small family farms due to high implementation costs. In particular, the selection of laying hens, an essential process for productivity, is still performed manually and requires considerable time and effort. This study presents the development of a modular, low-cost, and minimally invasive IoT system for the automatic detection of laying hens in family-run poultry farms. Additionally, the system enables environmental monitoring and utilizes LoRaWAN networks for efficient long-range data transmission. The collected data are stored on a centralized platform and integrated with web, mobile, and messaging applications to provide real-time access to information. The modular system architecture, developed using open-source software, ensures replicability, scalability, and adaptability to different production environments. The feasibility of the system has been validated through field trials in a real-world environment, demonstrating effective performance, low implementation costs, and high farmer satisfaction, with the user highlighting its positive impact on poultry farm management. Full article
(This article belongs to the Special Issue Technologies and Techniques for the Enhancement of Agriculture 4.0)
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30 pages, 21484 KiB  
Article
The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
by Cosmina-Mihaela Rosca, Adrian Stancu and Marian Popescu
Appl. Sci. 2025, 15(7), 4016; https://doi.org/10.3390/app15074016 - 5 Apr 2025
Cited by 1 | Viewed by 457
Abstract
Technological advancements in the cloud field are becoming widely used on a large scale in increasing activity sectors. Agriculture is an important domain in everyday life, central to human existence. This research comparatively analyzes two proposed types of infrastructures that optimize the growth [...] Read more.
Technological advancements in the cloud field are becoming widely used on a large scale in increasing activity sectors. Agriculture is an important domain in everyday life, central to human existence. This research comparatively analyzes two proposed types of infrastructures that optimize the growth flow of plants in a hydroponic system for continuous monitoring, one full-cloud and one full-local. The study’s main objective is to determine which of the two infrastructures is more suitable for the hydroponic scenario by conducting seven types of tests. This research aims to fill a gap in the specialized literature through a detailed analysis of the configuration, implementation methods, and all implications of the two approaches from the perspective of the seven indicators. The seven indicators are response time, operational reliability, implementation costs, operational costs, configuration scalability, data accessibility, and data security. The cloud infrastructure uses Microsoft Azure technologies, while the local variant uses custom-made scripts and locally installed services. For both software infrastructures, the hardware components are identical, including an M5Stack module with sensors for monitoring temperature, humidity, electrical conductivity, and liquid level in the hydroponic container. The test results highlight that the local infrastructure offers a shorter response time (200 ms compared to 300 ms for the cloud infrastructure). The results also showed lower operational costs for the local infrastructure, making it more suitable for autonomous hydroponic systems. On the other hand, the results showed that cloud infrastructure has greater data accessibility than local infrastructure, and the security measures are advanced. These advantages of cloud infrastructure involve recurring costs of USD 82.57/month. The limitations of this research are associated with the exclusion of human errors and cybernetics simulations from the analysis. Another limitation concerns the real analysis of short-term costs. Future research will explore the real fluctuations of long-term costs. Additionally, infrastructure studies on different plant species and hydroponic farms will also be considered. Full article
(This article belongs to the Special Issue Technologies and Techniques for the Enhancement of Agriculture 4.0)
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15 pages, 3028 KiB  
Article
Prediction of Tuber Damage from Harvesting and Processing Machine Working Units Based on the Recording of Impact Parameters
by Michał Sypuła, Aleksander Lisowski, Jacek Klonowski, Tomasz Nowakowski, Jarosław Chlebowski and Magdalena Dąbrowska
Appl. Sci. 2025, 15(3), 1161; https://doi.org/10.3390/app15031161 - 24 Jan 2025
Viewed by 567
Abstract
The paper presents the development of an empirical mathematical model of the potato tuber damage index that considers the relationship between impact parameters, such as peak acceleration, velocity change, and the experimental coefficient. This coefficient was developed using statistical analysis methods for four [...] Read more.
The paper presents the development of an empirical mathematical model of the potato tuber damage index that considers the relationship between impact parameters, such as peak acceleration, velocity change, and the experimental coefficient. This coefficient was developed using statistical analysis methods for four isolated surfaces and two potato varieties, Hermes and Saturna. The IRD 400 device was used to measure impacts, which recorded peak accelerations and changes in impact velocity at initial velocities in the 2.43–4.43 m·s−1 range. The study’s results indicate that the variety, type of surface, and initial impact velocity had a statistically significant effect on the tuber damage index; the type of surface and initial impact velocity had a statistically significant effect on the peak acceleration values (p < 0.05). The increase in peak acceleration with increases in the impact velocity confirms the hypothesis that the maximum force and the resulting internal stresses of the tuber are key elements causing damage due to the impact. The highest values of the tuber damage index occurred during impacts with steel surfaces and conveyor bars. The developed model allows for the faster prediction of potential damage in harvesting and post-harvest processing conditions than traditional measurement methods. To fully use the proposed model in Agriculture 4.0, further research should be conducted to improve recording devices for the measurement of impact parameters in real time. Full article
(This article belongs to the Special Issue Technologies and Techniques for the Enhancement of Agriculture 4.0)
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18 pages, 1975 KiB  
Article
Comparison of the Degree of Proteolytic Modification in Wheat Malts Obtained from Wheat Grain Produced at Different Nitrogen Fertilization Rates
by Justyna Belcar and Józef Gorzelany
Appl. Sci. 2024, 14(23), 11388; https://doi.org/10.3390/app142311388 - 6 Dec 2024
Cited by 1 | Viewed by 669
Abstract
The degree of proteolytic modification in wheat malts significantly affects their quality, determining their suitability for use in brewing. Nitrogen fertilization at doses of 60 and 80 kg N·ha−1 applied during 3-year field experiments had a positive effect on the content of [...] Read more.
The degree of proteolytic modification in wheat malts significantly affects their quality, determining their suitability for use in brewing. Nitrogen fertilization at doses of 60 and 80 kg N·ha−1 applied during 3-year field experiments had a positive effect on the content of protein compounds in the analyzed wheat malts. The total protein content of the malts obtained averaged 9.9–10.1% d. m., depending on the cultivar, and from 9.1 to 10.8% dry matter, depending on nitrogen fertilization. The soluble protein content and Kolbach index value were at similar levels (4.60% dry matter and 46.07%, respectively, on average) except for malt obtained from the Elixer cultivar, for which significantly lower values were obtained (by 7.39% and 6.66%, respectively). In contrast, the highest free amino nitrogen value was obtained for malts derived from the Elixer and Gimantis cultivars. The least variation in the quality of malts derived from grains obtained in the three growing seasons during the field experiments was in the cultivar Gimantis. In 2-year commodity field experiments (in commodity farms), under 60 kg N·ha−1 nitrogen fertilization, the cultivar Lawina was characterized by the best indices of the degree of proteolytic modification. Full article
(This article belongs to the Special Issue Technologies and Techniques for the Enhancement of Agriculture 4.0)
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