Submit to Agronomy Review for Agronomy Propose a Special Issue

Journal Browser

Smart Farming in Service of Modernizing Agriculture

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 33359

Share This Special Issue

Special Issue Editors

Dr. Spyros Fountas

E-Mail Website
Guest Editor

Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Interests: precision agriculture; UAV; information systems; farm machinery
Special Issues, Collections and Topics in MDPI journals

Dr. Thanos Balafoutis

E-Mail Website
Guest Editor

Center for Research and Technology Hellas (CERTH), Institute for Bio-economy and Agri-technology (iBO), Charilaou-Thermi Rd., 57001 Thessaloniki, Greece
Interests: energy in agriculture; renewable energy; precision agriculture; conservation agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart farming represents the application of modern information and communication technologies (ICT) to agriculture, and is based on a precise and resource-efficient approach attempting to sustainably achieve higher efficiency in agricultural goods production with increased quality. Smart farming includes farm management information systems (FMIS), precision agriculture (PA) systems, and agricultural automation and robotics. FMISs mainly represent software systems for collecting, processing, storing, and disseminating data in the form required to carry out a farm’s operations and functions. PA refers to the farming management concept aimed at optimizing input use based on recording technologies to observe and measure inter- and intra-field spatial and temporal variability in crops, aiming to improve economic returns and reduce environmental impact. Reacting technologies based on agricultural automation and robotics are separate but closely related ICT sectors that cover the process of applying automatic control, artificial intelligence techniques, and robotic platforms at all levels of agricultural production.

This Special Issue aims to discuss various aspects of smart farming applications, technologies, and management methods. This will include the state-of-the-art technologies applied in different cropping systems, agronomic models to interpret precision agriculture measured data, economic implications and adoption of smart farming in all agricultural activities (e.g., precision irrigation, precision fertilization, and selective harvesting). We invite the submission of studies on applications regarding all cropping systems.

We invite you to contribute to this Issue by submitting comprehensive reviews, case studies, or research articles that focus on scientific methods, technological tools, and innovative statistical analyses, in order to provide an opportunity for learning the state-of-the-art and for the discussion of future directions in smart farming. Papers selected for this Special Issue are subject to a rigorous peer-review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Dr. Spyros Fountas
Dr. Thanos Balafoutis
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. Agronomy 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 2600 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

Smart farming
Modern agriculture, precision agriculture
Agricultural automation
Environmental impact
Agricultural production

Published Papers (6 papers)

Download All Papers

Research

Jump to: Review

31 pages, 8196 KiB

Open AccessArticle

Ontology-Based IoT Middleware Approach for Smart Livestock Farming toward Agriculture 4.0: A Case Study for Controlling Thermal Environment in a Pig Facility

by Eleni Symeonaki, Konstantinos G. Arvanitis, Dimitrios Piromalis, Dimitrios Tseles and Athanasios T. Balafoutis

Agronomy 2022, 12(3), 750; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12030750 - 21 Mar 2022

Cited by 17 | Viewed by 3878

Abstract

Integrated farm management (IFM) is promoted as a whole farm approach toward Agriculture 4.0, incorporating smart farming technologies for attempting to limit livestock production’s negative impacts on the environment while increasing productivity with regard to the economic viability of rural communities. The Internet of Things (IoT) may serve as an enabler to ensure key properties—such as interconnectivity, scalability, agility, and interoperability—in IFM systems so that they could provide object-based services while adapting to dynamic changes. This paper focuses on the problem of facilitating the management, processing, and sharing of the vast and heterogeneous data points generated in livestock facilities by introducing distributed IoT middleware as the core of a responsive and adaptive service-oriented IFM system, specifically targeted to enable smart livestock farming in view of its unique requirements. The proposed IoT middleware encompasses the context-awareness approach via the integration of a flexible ontology-based structure for modeling and reasoning. The IoT middleware was assessed in actual conditions on the grounds of a case study for smart control of the thermal environment in a medium-sized pig farming facility. As derived from the obtained evaluation results, the system appears to perform quite satisfactorily in terms of computational performance as well as ontology coherence, consistency, and efficiency. Full article

(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)

► Show Figures

Figure 1

13 pages, 771 KiB

Open AccessArticle

Current Skills of Students and Their Expected Future Training Needs on Precision Agriculture: Evidence from Euro-Mediterranean Higher Education Institutes

by Thomas Bournaris, Manuela Correia, Alessandro Guadagni, Jeremy Karouta, Anne Krus, Stefania Lombardo, Dimitra Lazaridou, Efstratios Loizou, José Rafael Marques da Silva, Jorge Martínez-Guanter, Anastasios Michailidis, Stefanos Nastis, Aikaterini Paltaki, Maria Partalidou, Manuel Pérez-Ruiz, Ángela Ribeiro, Constantino Valero and Marco Vieri

Agronomy 2022, 12(2), 269; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12020269 - 21 Jan 2022

Cited by 9 | Viewed by 3276

Abstract

This paper set out to explore the precision agriculture (PA)-training needs of students studying in agricultural universities in the Euro-Mediterranean region (Greece, Italy, Portugal and Spain). SPARKLE is a Knowledge Alliance Project, funded by the European Union (EU), and one of its main goals is to narrow the innovation divide between entrepreneurship and the effective application of sustainable PA. During the project, the research conducted in all countries in the Euro-Mediterranean region revealed differences in the PA-training needs of university students. Additionally, this paper set out to explore the socioeconomic characteristics of students that affect their interest and knowledge towards PA. Finally, this paper aimed to understand the scope, present status and strategies for improving PA training in agricultural universities in the Euro-Mediterranean region. The following descriptive statistics and two multivariate analysis techniques were used: Two-Step Cluster Analysis (TSCA) and Categorical Regression (CATREG). Results support the notion that the lack of “PA knowledge/interest” adds to the technological gap amongst university students, slow adoption of PA and lower levels of overall rural economic development. These findings will be used as the fundamental cognition for the development of a joint action plan and several other national plans in the selected regions. Full article

(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)

► Show Figures

Figure 1

15 pages, 2241 KiB

Open AccessArticle

Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making

by Guan-Sin Li, Dong-Hong Wu, Yuan-Chih Su, Bo-Jein Kuo, Ming-Der Yang, Ming-Hsin Lai, Hsiu-Ying Lu and Chin-Ying Yang

Agronomy 2021, 11(8), 1626; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11081626 - 16 Aug 2021

Cited by 2 | Viewed by 2611

Abstract

Rice is a staple food crop in Asia. The rice farming industry has been influenced by global urbanization, rapid industrialization, and climate change. A combination of precise agricultural and smart water management systems to investigate the nutrition state in rice is important. Results indicated that plant nitrogen and chlorophyll content at the maximum tillering stage were significantly influenced by the interaction between water and fertilizer. The normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE), obtained from the multispectral images captured by a UAV, exhibited the highest positive correlations (0.83 and 0.82) with plant nitrogen content at the maximum tillering stage. The leave-one-out cross-validation method was used for validation, and a final plant nitrogen content prediction model was obtained. A regression function constructed using a nitrogen nutrition index and the difference in field cumulative nitrogen had favorable variation explanatory power, and its adjusted coefficient of determination was 0.91. We provided a flow chart showing how the nutrition state of rice can be predicted with the vegetation indices obtained from UAV image analysis. Differences in field cumulative nitrogen can be further used to diagnose the demand of nitrogen topdressing during the panicle initiation stage. Thus, farmers can be provided with precise panicle fertilization strategies for rice fields. Full article

(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)

► Show Figures

Figure 1

26 pages, 17645 KiB

Open AccessEditor’s ChoiceArticle

Remote and Proximal Sensing-Derived Spectral Indices and Biophysical Variables for Spatial Variation Determination in Vineyards

by Nicoleta Darra, Emmanouil Psomiadis, Aikaterini Kasimati, Achilleas Anastasiou, Evangelos Anastasiou and Spyros Fountas

Agronomy 2021, 11(4), 741; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040741 - 11 Apr 2021

Cited by 23 | Viewed by 3406

Abstract

Remote-sensing measurements are crucial for smart-farming applications, crop monitoring, and yield forecasting, especially in fields characterized by high heterogeneity. Therefore, in this study, Precision Viticulture (PV) methods using proximal- and remote-sensing technologies were exploited and compared in a table grape vineyard to monitor and evaluate the spatial variation of selected vegetation indices and biophysical variables throughout selected phenological stages (multi-seasonal data), from veraison to harvest. The Normalized Difference Vegetation Index and the Normalized Difference Red-Edge Index were calculated by utilizing satellite imagery (Sentinel-2) and proximal sensing (active crop canopy sensor Crop Circle ACS-470) to assess the correlation between the outputs of the different sensing methods. Moreover, numerous vegetation indices and vegetation biophysical variables (VBVs), such as the Modified Soil Adjusted Vegetation Index, the Normalized Difference Water Index, the Fraction of Vegetation Cover, and the Fraction of Absorbed Photosynthetically Active Radiation, were calculated, using the satellite data. The vegetation indices analysis revealed different degrees of correlation when using diverse sensing methods, various measurement dates, and different parts of the cultivation. The results revealed the usefulness of proximal- and remote-sensing-derived vegetation indices and variables and especially of Normalized Difference Vegetation Index and Fraction of Absorbed Photosynthetically Active Radiation in the monitoring of vineyard condition and yield examining, since they were demonstrated to have a very high degree of correlation (coefficient of determination was 0.87). The adequate correlation of the vegetation indices with the yield during the latter part of the veraison stage provides valuable information for the future estimation of production in broader areas. Full article

(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)

► Show Figures

Figure 1

12 pages, 4685 KiB

Open AccessArticle

Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

by Hong-Kun Lyu, Sanghun Yun and Byeongdae Choi

Agronomy 2020, 10(12), 1926; https://doi.org/10.3390/agronomy10121926 - 08 Dec 2020

Cited by 5 | Viewed by 2743

Abstract

Deep learning and machine learning (ML) technologies have been implemented in various applications, and various agriculture technologies are being developed based on image-based object recognition technology. We propose an orchard environment free space recognition technology suitable for developing small-scale agricultural unmanned ground vehicle (UGV) autonomous mobile equipment using a low-cost lightweight processor. We designed an algorithm to minimize the amount of input data to be processed by the ML algorithm through low-resolution grayscale images and image binarization. In addition, we propose an ML feature extraction method based on binary pixel quantification that can be applied to an ML classifier to detect free space for autonomous movement of UGVs from binary images. Here, the ML feature is extracted by detecting the local-lowest points in segments of a binarized image and by defining 33 variables, including local-lowest points, to detect the bottom of a tree trunk. We trained six ML models to select a suitable ML model for trunk bottom detection among various ML models, and we analyzed and compared the performance of the trained models. The ensemble model demonstrated the best performance, and a test was performed using this ML model to detect apple tree trunks from 100 new images. Experimental results indicate that it is possible to recognize free space in an apple orchard environment by learning using approximately 100 low-resolution grayscale images. Full article

(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)

► Show Figures

Figure 1

Review

Jump to: Research

37 pages, 1476 KiB

Open AccessEditor’s ChoiceReview

Characterising the Agriculture 4.0 Landscape—Emerging Trends, Challenges and Opportunities

by Sara Oleiro Araújo, Ricardo Silva Peres, José Barata, Fernando Lidon and José Cochicho Ramalho

Agronomy 2021, 11(4), 667; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11040667 - 01 Apr 2021

Cited by 100 | Viewed by 15797

Abstract

Investment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sector. Full article

(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)

► Show Figures