Submit to Special Issue Submit Abstract to Special Issue Review for AgriEngineering Propose a Special Issue

Journal Menu

Journal Browser

Digital Agriculture 5.0: A New Perspective in Agricultural Engineering

Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 6495

Share This Special Issue

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

16 pages, 6288 KB

Open AccessArticle

Reducing Within-Vineyard Spatial Variability Through Real-Time Variable-Rate Fertilization: A Case Study in the Conegliano Valdobbiadene Prosecco DOCG Region

by Marco Sozzi, Davide Boscaro, Alessandro Zanchin, Francesco Marinello and Diego Tomasi

AgriEngineering 2025, 7(9), 280; https://doi.org/10.3390/agriengineering7090280 - 29 Aug 2025

Viewed by 266

Abstract

Spatial variability in vine vigour and yield components is a major challenge for vineyard management and consistent grape quality, particularly in hilly landscapes. This study evaluates the impact of on-the-go variable-rate fertilisation (VRA) in reducing within-vineyard variability in an 8.5 hectares commercial vineyard in the Conegliano Valdobbiadene Prosecco DOCG region (Italy). Over two growing seasons, a proximal NDVI sensor (GreenSeeker) guided real-time fertiliser applications without prescription maps. Vine vigour, yield components, and grape quality were evaluated using geostatistical analysis and coefficient of variation (CV) metrics. VRA reduced total spatial variability (sill) by 55% and erratic variance (nugget effect) by 39% for NDVI measurements. Variability in yield components also decrease (−21.1% for cluster number, −6.25% for cluster weight), while grape composition parameters (total soluble solids, titratable acidity, and pH) was not significantly altered despite a slightly higher variability (in titratable acidity and pH), indicating that fertiliser modulation did not compromise grape quality. Nitrogen input was reduced by 50%, highlighting economic and environmental benefits (−302 kg CO₂). These results show that simplified, sensor-based, on-the-go VRA is a practical and sustainable precision viticulture tool, even in small and heterogeneous vineyards typical of the Conegliano Valdobbiadene Prosecco DOCG area. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Figure 1

18 pages, 4106 KB

Open AccessArticle

Automated IoT-Based Monitoring of Industrial Hemp in Greenhouses Using Open-Source Systems and Computer Vision

by Carmen Rocamora-Osorio, Fernando Aragon-Rodriguez, Ana María Codes-Alcaraz and Francisco-Javier Ferrández-Pastor

AgriEngineering 2025, 7(9), 272; https://doi.org/10.3390/agriengineering7090272 - 22 Aug 2025

Viewed by 872

Abstract

Monitoring the development of greenhouse crops is essential for optimising yield and ensuring the efficient use of resources. A system for monitoring hemp (Cannabis sativa L.) cultivation under greenhouse conditions using computer vision has been developed. This system is based on open-source automation software installed on a single-board computer. It integrates various temperature and humidity sensors and surveillance cameras, automating image capture. Hemp seeds of the Tiborszallasi variety were sown. After germination, plants were transplanted into pots. Five specimens were selected for growth monitoring by image analysis. A surveillance camera was placed in front of each plant. Different approaches were applied to analyse growth during the early stages: two traditional computer vision techniques and a deep learning algorithm. An average growth rate of 2.9 cm/day was determined, corresponding to 1.43 mm/°C day. A mean MAE value of 1.36 cm was obtained, and the results of the three approaches were very similar. After the first growth stage, the plants were subjected to water stress. An algorithm successfully identified healthy and stressed plants and also detected different stress levels, with an accuracy of 97%. These results demonstrate the system’s potential to provide objective and quantitative information on plant growth and physiological status. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Figure 1

16 pages, 2852 KB

Open AccessArticle

Ear Back Surface Temperature of Pigs as an Indicator of Comfort: Spatial Variability and Its Thermal Implications

by Taize Calvacante Santana, Cristiane Guiselini, Héliton Pandorfi, Ricardo Brauer Vigoderis, José Antônio Delfino Barbosa Filho, Rodrigo Gabriel Ferreira Soares, Maria de Fátima Araújo Alves, Marco Antonio Silva, Leandro Dias de Lima and João José de Mesquita Sales

AgriEngineering 2025, 7(8), 266; https://doi.org/10.3390/agriengineering7080266 - 19 Aug 2025

Viewed by 395

Abstract

This study applied geostatistics to analyze thermal images of the back surface of pigs’ ears (TSO) to understand how spatial temperature variability influences thermoregulation. The objective was to assess TSO variability in pigs housed under two climate control systems, namely, pens without cooling (BTEST) and with an evaporative cooling system (BECS), using infrared thermography and geostatistical tools. A total of 432 thermal images were obtained from 18 finishing pigs at 08:00, 12:00, and 16:00. Semivariograms were modeled and validated, and kriging maps were developed to visualize the spatial temperature distribution. The pens were thermally characterized using reclassified Temperature and Humidity Index (THI) values. The Gaussian model (R² > 0.9) showed strong spatial dependence in temperature data. Pigs in the BECS system exhibited lower average TSO temperatures (28.2–38.6 °C) than those in the BTEST system, where temperatures exceeded 34 °C, highlighting the role of cooling in mitigating heat stress. In both systems, higher THI values were associated with increased TSO, indicating thermal discomfort under elevated environmental temperatures. Geostatistical analysis effectively revealed spatial patterns and variability in surface temperatures, providing key insights into how environmental conditions impact pigs’ thermal responses. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Figure 1

15 pages, 2127 KB

Open AccessArticle

Relationship Between Hyperspectral Data and Amino Acid Composition in Soybean Genotypes

by Ana Carina da Silva Cândido Seron, Dthenifer Cordeiro Santana, Izadora Araujo Oliveira, Cid Naudi Silva Campos, Larissa Pereira Ribeiro Teodoro, Elber Vinicius Martins Silva, Rafael Felippe Ratke, Fábio Henrique Rojo Baio, Carlos Antonio da Silva Junior and Paulo Eduardo Teodoro

AgriEngineering 2025, 7(8), 265; https://doi.org/10.3390/agriengineering7080265 - 15 Aug 2025

Viewed by 366

Abstract

Spectral reflectance of plants can be readily associated with physiological and biochemical parameters. Thus, relating spectral data to amino acid contents in different genetic materials provides an innovative and efficient approach for understanding and managing genetic diversity. Therefore, this study had two objectives: (I) to differentiate genetic materials according to amino acid contents and spectral reflectance; (II) to establish the relationship between amino acids and spectral bands derived from hyperspectral data. The research was conducted with 32 soybean genetic materials grown in the field during the 2023–2024 crop year. The experimental design involved randomized blocks with four replicates. Leaf spectral data were collected 60 days after plant emergence, when the plants were in full bloom. Three leaf samples were collected from the third fully developed trifoliate leaf, counted from top to bottom, from each plot. The samples were taken to the laboratory, where reflectance readings were obtained using a spectroradiometer, which can measure the 350–2500 nm spectrum. Wavelengths were grouped as means of representative intervals and then organized into 28 bands. Subsequently, the leaf samples from each plot were subjected to quantification analyses for 17 amino acids. Then, the soybean genotypes were subjected to a PCA–K-means analysis to separate the genotypes according to their amino acid content and spectral behavior. A correlation network was constructed to investigate the relationships between the spectral variables and between the amino acids within each group. The groups formed by the different genetic materials exhibited distinct profiles in both amino acid composition and spectral behavior. Leaf reflectance data proved to be efficient in identifying differences between soybean genotypes regarding the amino acid content in the leaves. Leaf reflectance was effective in distinguishing soybean genotypes according to leaf amino acid content. Specific and high-magnitude associations were found between spectral bands and amino acids. Our findings reveal that spectral reflectance can serve as a reliable, non-destructive indicator of amino acid composition in soybean leaves, supporting advanced phenotyping and selection in breeding programs. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Figure 1

20 pages, 3422 KB

Open AccessArticle

Field Spectroscopy for Monitoring Nitrogen Fertilization and Estimating Cornstalk Nitrate Content in Maize

by Jesús Val, Iván González-Pérez, Enoc Sanz-Ablanedo, Ángel Maresma and José Ramón Rodríguez-Pérez

AgriEngineering 2025, 7(8), 264; https://doi.org/10.3390/agriengineering7080264 - 14 Aug 2025

Viewed by 336

Abstract

Evaluating the response of maize crops to different nitrogen fertilization rates is essential to ensure their agronomic, environmental, and economic efficiency. In this study, the spectral information of maize plants subjected to five distinct nitrogen fertilization strategies was analyzed. The fertilization strategies were based on the practices commonly used in maize fields in the study area, with the aim of ensuring the research findings’ applicability. The spectral reflectance was measured using a spectroradiometer covering the 350–2500 nm range, and the results enabled the identification of optimal spectral regions for monitoring plants’ nitrogen status, particularly in the visible and infrared ranges. A Principal Component Analysis (PCA) of the reflectance data revealed the key wavelengths most sensitive to the nitrogen availability: 555 nm and 720 nm during the vegetative stage and 680 nm during the reproductive stage. This information will support the development of drone-mounted multispectral sensor systems for large-scale monitoring, as well as the design of low-cost sensors for early nitrogen deficiency detection. Furthermore, the study demonstrated the feasibility of estimating the cornstalk nitrate content based on direct reflectance measurements of maize stems. The prediction model showed satisfactory performance, with a coefficient of determination (R²) of 0.845 and a root mean square error of prediction (RMSECV) of 2035.3 ppm, indicating its strong potential for predicting the NO₃-N concentrations in maize stems. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Figure 1

14 pages, 2579 KB

Open AccessArticle

Prediction of Subcutaneous Fat Thickness (SFT) in Pantaneiro Lambs: A Model Based on Adipometer and Body Measurements for Android Application

by Adrielly Lais Alves da Silva, Marcus Vinicius Porto dos Santos, Marcelo Corrêa da Silva, Hélio Almeida Ricardo, Marcio Rodrigues de Souza, Núbia Michelle Vieira da Silva and Fernando Miranda de Vargas Junior

AgriEngineering 2025, 7(8), 251; https://doi.org/10.3390/agriengineering7080251 - 7 Aug 2025

Viewed by 784

Abstract

The increasing adoption of digital technologies in the agriculture sector has significantly contributed to optimizing on-farm routines, especially in data-driven decision-making. This study aimed to develop an application to determine the slaughter point of lambs by predicting subcutaneous fat thickness (SFT) using pre-slaughter parameters such as body weight (BW), body condition score (BCS), and skinfold measurements at the brisket (BST), lumbar (LST), and tail base (TST), obtained using an adipometer. A total of 45 Pantaneiros lambs were evaluated, finished in feedlot, and slaughtered at different body weights. Each pre-slaughter weight class showed a distinct carcass pattern when all parameters were included in the model. Exploratory analysis revealed statistical significance for all variables (p < 0.001). BW and LST were selected to construct the predictive equation (R² = 55.44%). The regression equations were integrated into the developed application, allowing for in-field estimation of SFT based on simple measurements. Compared to conventional techniques such as ultrasound or visual scoring, this tool offers advantages in portability, objectivity, and immediate decision-making support. In conclusion, combining accessible technologies (e.g., adipometer) with traditional variables (e.g., body weight), represents an effective alternative for production systems aimed at optimizing and enhancing the value of lamb carcasses. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Graphical abstract

22 pages, 2425 KB

Open AccessArticle

Spatial Variability in the Deposition of Herbicide Droplets Sprayed Using a Remotely Piloted Aircraft

by Edney Leandro da Vitória, Luis Felipe Oliveira Ribeiro, Ivoney Gontijo, Fábio Ribeiro Pires, Aloisio José Bueno Cotta, Francisco de Assis Ferreira, Marconi Ribeiro Furtado Júnior, Maria Eduarda da Silva Barbosa, João Victor Oliveira Ribeiro and Josué Wan Der Maas Moreira

AgriEngineering 2025, 7(8), 245; https://doi.org/10.3390/agriengineering7080245 - 1 Aug 2025

Viewed by 524

Abstract

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

(This article belongs to the Special Issue Digital Agriculture 5.0: A New Perspective in Agricultural Engineering)

► Show Figures

Figure 1

17 pages, 2245 KB

Open AccessArticle

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 637

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 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)

► Show Figures

Figure 1

16 pages, 1657 KB

Open AccessArticle

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 476

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 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)

► Show Figures

Figure 1

29 pages, 3351 KB

Open AccessArticle

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 545

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 (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)

► Show Figures

Figure 1

Review

Jump to: Research

18 pages, 1178 KB

Open AccessReview

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 627

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 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)

► Show Figures

Journal Menu

Journal Browser

Digital Agriculture 5.0: A New Perspective in Agricultural Engineering

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI