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AgriEngineering
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Digital Agriculture 5.0: A New Perspective in Agricultural Engineering
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Digital Agriculture 5.0: A New Perspective in Agricultural Engineering

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1347

Special Issue Editors

Prof. Dr. Marcos Vinícius Da Silva

E-Mail Website
Guest Editor
Centro de Ciências Agrárias e Ambientais (CCAA), Universidade Federal de Maranhão, BR-222, Chapadinha 65500-000, MA, Brazil
Interests: animal confort; agricultural and biosystems engineering; agricultural engineering; agricultural meteorology; animal thermal comfort; applied meteorology; applied statistics; hydrological modeling; hydrology; irrigation engineering; remote sensing; meteorology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nítalo Andre Farias Machado

E-Mail Website
Guest Editor
Centro de Ciências Agrárias e Ambientais (CCAA), Universidade Federal de Maranhão, BR-222, Chapadinha 65500-000, MA, Brazil
Interests: biometeorology; climate-smart livestock production systems; animal transport; crop-livestock interaction; animal welfare; heat tolerance; thermal equilibrium; infrared thermography; agricultural engineering; biosystems engineering; precision livestock farming; misuse of land
Dr. Airton Goncalves de Oliveira

E-Mail Website
Guest Editor
Centro de Ciências Agrárias e Ambientais (CCAA), Universidade Federal de Maranhão, BR-222, Chapadinha 65500-000, MA, Brazil
Interests: rural constructions; animal environment; environmental impact assessment; environmental pollution; sanitation and socio-environmental vulnerability
Dr. Jhon Lennon Bezerra Da Silva

E-Mail Website1 Website2
Guest Editor
Cerrado Irrigation Graduate Program, Goiano Federal Institute-Campus Ceres, GO-154, km 218-Zona Rural, Ceres 76300-000, Goiás, Brazil
Interests: remote sensing; satellite images; climate change; radiation and energy balance; vegetation and water indices; evapotranspiration; rainfall and drought
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The present Special Issue proposal, entitled "Digital Agriculture 5.0: A New Perspective in Agricultural Engineering", focuses on discussing the advances, challenges, and prospects of Agriculture 5.0 within the context of engineering applied to the agricultural sector. With the rise in digital technologies, Agriculture 5.0 emerges as a new revolution in the field, incorporating elements of artificial intelligence, the Internet of Things (IoT), cloud computing, autonomous robotics, big data, and predictive analytics to promote a more efficient, sustainable, secure, and personalized agricultural production.

The scope of this Special Issue includes theoretical, experimental, and applied studies that explore technological innovations aimed at enhancing agricultural production, intelligent management of natural resources, process automation, and real-time data-driven decision-making. Contributions are welcome that involve the development of smart sensors, integration of digital platforms in agricultural machinery, remote monitoring systems, predictive modeling, and emerging technologies applied to precision and regenerative agriculture.

The purpose of this issue is to compile a comprehensive body of research and practical applications that reflect the transition from the conventional model to the digital paradigm of Agriculture 5.0, expanding the discussion on how such technologies can transform not only productivity but also the sustainability and resilience of agri-food systems in the face of global challenges such as climate change and population growth.

This Special Issue also aims to complement the existing literature on Agriculture 4.0 and traditional agricultural engineering by advancing toward a more integrated and intelligent perspective, involving the use of cognitive, adaptive, and interconnected technologies. By bringing together different approaches and case studies at multiple scales, the issue will significantly contribute to the advancement of technical-scientific knowledge in the field, fostering interdisciplinary dialog between engineering, data science, agronomy, and sustainability.

Prof. Dr. Marcos Vinícius Da Silva
Prof. Dr. Nítalo Andre Farias Machado
Dr. Airton Goncalves de Oliveira
Dr. Jhon Lennon Bezerra Da Silva
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 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

  • agriculture 5.0
  • agricultural engineering
  • digital agriculture
  • agricultural automation
  • artificial intelligence
  • internet of things (IoT)
  • big data
  • data-driven decision-making
  • machine learning applied to remote sensing
  • precision agriculture
  • emerging technologies

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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

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17 pages, 2245 KiB  
Article
Digital Environmental Management of Heat Stress Effects on Milk Yield and Composition in a Portuguese Dairy Farm
by Daniela Pinto, Rute Santos, Carolina Maia, Ester Bartolomé, João Niza-Ribeiro, Maria Cara d’ Anjo, Mariana Batista and Luís Alcino Conceição
AgriEngineering 2025, 7(7), 231; https://doi.org/10.3390/agriengineering7070231 - 10 Jul 2025
Viewed by 241
Abstract
Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located [...] Read more.
Heat stress has been identified as one of the main challenges for dairy production systems, particularly in the context of global warming. This one-year study aimed to evaluate the impact of heat stress on milk yield and composition in a dairy farm located in the Elvas region of Portugal. A pack of electronic sensors was installed in the lactating animal facilities, allowing continuous recording of environmental data (temperature, humidity, ammonia and carbon dioxide). Based on these data, the Temperature-Humidity Index (THI) was automatically calculated on a daily basis, with the values subsequently aggregated into 7-day moving averages and integrated with milk production records, somatic cell count, and milk fat and protein content. The results indicate a significant influence of THI on both milk yield and composition, particularly on protein and fat content. The relationships between the variables were found to be non-linear, which contrasts with some results described in the literature. These discrepancies may be related to genetic differences between animals, variations in diets, production levels, management conditions, or the statistical models used in previous studies. Dry matter intake proved to be an important predictive variable. These findings reinforce the importance of ensuring animal welfare through continuous environmental monitoring and the implementation of effective heat stress mitigation strategies in the dairy sector. Full article
(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)
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16 pages, 1657 KiB  
Article
Ecophysiological Management Using Light Interception Technology with the AccuPar Equipment: Quality Versus Quantity of Forage
by Anderson de Moura Zanine, Tomaz Melo Neto, Daniele de Jesus Ferreira, Edson Mauro Santos, Henrique Nunes Parente, Michelle Oliveira Maia Parente, Francisco Naysson de Sousa Santos, Fleming Sena Campos, Francisca Claudia Silva Sousa, Sara Silva Reis, Dilier Olivera-Viciedo and Arlan Araújo Rodrigues
AgriEngineering 2025, 7(7), 224; https://doi.org/10.3390/agriengineering7070224 - 8 Jul 2025
Viewed by 242
Abstract
Background: Understanding canopy light interception is essential for optimizing forage production and improving the efficiency of grazing systems. Accurate quantification of photosynthetically active radiation (PAR) intercepted by the canopy allows for better estimation of crop coefficients and growth dynamics. This study aimed to [...] Read more.
Background: Understanding canopy light interception is essential for optimizing forage production and improving the efficiency of grazing systems. Accurate quantification of photosynthetically active radiation (PAR) intercepted by the canopy allows for better estimation of crop coefficients and growth dynamics. This study aimed to assess the forage mass and nutritional value of Guinea grass pastures managed under two grazing frequencies, defined by 90% and 95% light interception (LI) measured using AccuPar equipment, and two post-grazing stubble heights (30 and 50 cm). Evaluations were conducted during both the rainy season and a dry year to capture seasonal variability in pasture performance. Methods: The experimental design was of completely randomized blocks with four replications. Results: The treatment whit 90% LI resulted in higher values of crude protein and digestible. However, 95% LI resulted in higher values of neutral detergent insoluble nitrogen and acid detergent insoluble nitrogen values in grass pastures Guinea. The highest value of forage mass in Guinea grass was reported with 95% LI in association with a post-grazing height of 30 cm. Conclusions: Management of light interception at 90% provided a reduced amount of forage with better nutritional value. Pasture management considering the light interception technology with the AccuPar equipment was efficient as a pattern for interrupting pasture regrowth in the vegetative phase. Full article
(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)
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29 pages, 3351 KiB  
Article
Machine Learning in Estimating Daily Global Radiation in the Brazilian Amazon for Agricultural and Environmental Applications
by Charles Campoe Martim, Rhavel Salviano Dias Paulista, Daniela Roberta Borella, Frederico Terra de Almeida, João Gabriel Ribeiro Damian, Érico Tadao Teramoto and Adilson Pacheco de Souza
AgriEngineering 2025, 7(7), 216; https://doi.org/10.3390/agriengineering7070216 - 3 Jul 2025
Viewed by 238
Abstract
Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two machine learning [...] Read more.
Knowledge of global radiation (Hg) is essential for regional economic development and can help guide public policies related to agricultural and energy potential. However, its availability in several Brazilian regions is still limited. This work evaluates the predictive capacity of two machine learning (ML) techniques, such as multi-layer perceptrons (MLPs) and support vector machines (SVMs), in the estimation of Hg in 20 meteorological stations with 40 different input combinations involving insolation, air temperature, air relative humidity, photoperiod, and extraterrestrial radiation. It is also compared with three empirical models based on insolation, temperature, and a hybrid combination. In general, the greater the number of input variables, the better the performance of ML techniques, especially in combinations involving insolation that reduced the dispersion of estimated Hg on days with high atmospheric transmissivity and air temperature on days with low atmospheric transmissivity. The performance of SVM was better when compared to MLP in all statistical indicators. ML techniques presented better results than empirical models, and in general, the ordering of the best models in the three locations is achieved using SVM, MLP, and empirical models. Therefore, due to their easy implementation and generation of good results, the use of SVM models is recommended to estimate daily global radiation in the Brazilian Amazon. Full article
(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)
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Review

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18 pages, 1178 KiB  
Review
Research on Using Ensemble Models to Assess the Impacts of Climate Change on Agriculture Production: A Review
by Leonardo Pinto de Magalhães, Adriana Cavalieri Sais and Fabrício Rossi
AgriEngineering 2025, 7(7), 219; https://doi.org/10.3390/agriengineering7070219 - 7 Jul 2025
Viewed by 330
Abstract
The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim [...] Read more.
The use of artificial intelligence tools in agriculture is growing. In particular, the use of ensemble models. However, there are still few reviews on the use of these models in the study of the impacts of climate change on agriculture. Therefore, the aim of this article is to review the use of such models and perform three key tasks: (1) identify topics in which ensemble models are used, (2) determine the most widely applied model and the predominant crops and regions, and (3) explore future applications and challenges. As a result, it was noted that the first studies, dating back to 2011, applied ensemble models to model invasive species. Since then, research has focused on changes in temperature and precipitation, with at least one study published every year. The most cited studies have dealt with land use classification, emphasizing its relevance to climate change studies. Notably, studies on carbon storage in soil and its capacity to remove CO2 from the atmosphere have become increasingly relevant. This analysis highlights the growing importance of ensemble models in climate-related agricultural research, outlining trends and key areas for future exploration. Full article
(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)
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