Special Issue "Digital Transformation in the Agriculture Sector"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 October 2022 | Viewed by 4934

Special Issue Editors

Dr. Juan Antonio Martinez Navarro
E-Mail Website
Guest Editor
Department of Information and Communications Engineering, Computer Science Faculty, University of Murcia, Murcia, Spain
Interests: smart cities; smart agriculture; IoT; security; privacy; sensor networks; vehicular ad-hoc networks; mobile ad-hoc networks
Special Issues, Collections and Topics in MDPI journals
Dr. José Santa
E-Mail Website
Guest Editor
Department of Electronics, Computer Technology and Projects, School of Telecommunications Engineering, Technical University of Cartagena, 30202 Cartagena, Murcia, Spain
Interests: intelligent transportation systems; mobile and wireless networks; intelligent infrastructures and telematics
Special Issues, Collections and Topics in MDPI journals
Dr. Andrés Muñoz
E-Mail Website
Guest Editor
Polytechnic School, Catholic University of Murcia, Campus de los Jerónimos, 30107 Guadalupe, Murcia, Spain
Interests: knowledge engineering; semantic web; ambient intelligence; intelligent environments; context-awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Food and Agricultural Organization (FAO) of the United Nations has expressed its concern because of growing population and urbanization which has impacted in the global food production. Digital technologies can improve traditional agriculture by reducing water and nutrient consumption, avoiding the need of large crop areas and adjusting environmental parameters as desired, among others. Traditionally, the agriculture sector has integrated digital technologies for managing irrigation systems or forecasting weather conditions, but recent developments in the area of communications, computation and artificial intelligence can further improve areas such as precision agriculture and spread automatic management of crops worldwide.

Nowadays, Information and Communication Technologies (ICT) developments and their evolution are transforming the paradigm of agriculture production management providing with more and more precise information allowing the agriculture industry to better understand the behavior and development of the agriculture productions and enabling farmers to meet the requirements that these productions need in each of their stages. In this manner, digital agriculture can assist with optimizing the efficiency, providing decision support systems capable of estimating a future state of the production, giving the parameters of the current trends, or even interacting with other systems thanks to interoperability components making easier to spread an isolated knowledge to nearby productions thanks to cooperative approaches.

The goal of this Special Issue is to motivate the publication of works and studies in the field of the digital transformation of the agriculture section, with emphasis on IoT-enabled solutions, interoperability, distributed computing and machine-learning-based models providing their outputs as decision support systems. Therefore, researchers are invited to submit their manuscripts to this Special Issue and contribute their solutions, models, proposals, reviews and studies.

Potential topics include, but are not limited to:

  • IoT for smart agriculture.
  • Precision agriculture using ICT.
  • Sustainable agriculture using digital technologies.
  • Crop sensing.
  • Wireless sensor networks in agriculture.
  • Application of LPWAN communications in the agriculture sector.
  • Crop monitoring using satellites and UAVs.
  • Semantic models for smart agriculture.
  • Cloud, edge and fog computing in agriculture.
  • Intelligent resource allocation and decision-making tools.
  • Intelligent applications for ensuring food security.
  • Intelligent tools and methods for farming system design.
  • Intelligent and connected vehicles in smart agriculture.
  • Electronics in smart agriculture.
  • Wearable devices to support farmers.
  • Application of 5G/6G technologies and networks in smart agriculture.

Dr. Juan Antonio Martínez Navarro
Dr. José Santa
Dr. Andrés Muñoz
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. Electronics 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 2000 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 sensors
  • Smart agriculture
  • Machine-Learning- , AI – based models
  • Data analytics
  • Cloud/fog/edge computing
  • Remote sensing
  • Satellite and UAV image analysis and exploitation
  • Interoperability
  • Agriculture-related Ontologies
  • Resource allocation
  • Decision making
  • Food security
  • Farming system design

Published Papers (6 papers)

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Research

Article
Integration of Low-Cost Digital Tools for Preservation of a Sustainable Agriculture System
Electronics 2022, 11(6), 964; https://doi.org/10.3390/electronics11060964 - 21 Mar 2022
Viewed by 548
Abstract
This work presents an electronic sensing approach composed of a pair of Physical–Chemical and Imaging modules to preserve an aquaponic system. These modules offer constant measurements of the physical–chemical characteristics within the fish tank and the grow bed, and an indication of the [...] Read more.
This work presents an electronic sensing approach composed of a pair of Physical–Chemical and Imaging modules to preserve an aquaponic system. These modules offer constant measurements of the physical–chemical characteristics within the fish tank and the grow bed, and an indication of the health of the growing plants through image processing techniques. This proposal is implemented in a low-cost computer, receiving measurements from five sensors, including a camera, and processing the signals using open-source libraries and software. Periodic measurements of the temperature, water level, light, and pH within the system are collected and shared to a cloud platform that allows their display in a dashboard, accessible through a web page. The health of the vegetables growing in the system is estimated by analyzing visible and infrared spectra, applying feature extraction, and computing vegetation indices. This work provides a low-cost solution for preserving sustainable urban farming systems, suitable for new farming communities. Full article
(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)
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Article
Evaporation Forecasting through Interpretable Data Analysis Techniques
Electronics 2022, 11(4), 536; https://doi.org/10.3390/electronics11040536 - 10 Feb 2022
Viewed by 388
Abstract
Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the [...] Read more.
Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions. Full article
(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)
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Article
Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
Electronics 2022, 11(3), 356; https://doi.org/10.3390/electronics11030356 - 25 Jan 2022
Viewed by 900
Abstract
One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting [...] Read more.
One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms. Full article
(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)
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Article
Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing
Electronics 2022, 11(3), 325; https://doi.org/10.3390/electronics11030325 - 20 Jan 2022
Viewed by 579
Abstract
The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications [...] Read more.
The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications continuously for an agricultural year. This paper presents a generic methodology developed for implementing monitoring controls. To achieve this, the dataset provided by the Sentinel-2 time series is transformed into information through the combination of classifications with machine learning using random forest and remote sensing-based biophysical indices. This work focuses on monitoring the helpline associated with rice cultivation, using 13 Sentinel-2 images whose grouping and characteristics change depending on the event or landmark being sought. Moreover, the functionality to check, before harvesting the crop, that the area declared is equal to the area cultivated is added. The 2020 results are around 96% for most of the metrics analysed, demonstrating the potential of Sentinel-2 for controlling subsidies, particularly for rice. After the quality assessment, the hit rate is 98%. The methodology is transformed into a tool for regular use to improve decision making by determining which declarants comply with the crop-specific aid obligations, contributing to optimising the administrations’ resources and a fairer distribution of funds. Full article
(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)
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Article
Dairy Farm Management Information Systems
Electronics 2022, 11(2), 239; https://doi.org/10.3390/electronics11020239 - 12 Jan 2022
Cited by 1 | Viewed by 746
Abstract
Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the [...] Read more.
Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the Dutch dairy sector and present the state–of–the–art. We performed a multivocal literature review (MLR) to find sources both in scientific and grey literature. A grey literature search was adopted because most of the FMISs were not reported in the scientific literature. To support and improve the effectiveness of the MLR process, an online survey was first sent to Dutch dairy farmers to identify the FMISs that are being used in practice. With the help of the MLR process, we identified 50 FMISs used by Dutch dairy farmers. We identified 33 features of these FMISs and listed the advantages and disadvantages of the FMISs. Full article
(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)
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Article
Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model
Electronics 2021, 10(13), 1576; https://doi.org/10.3390/electronics10131576 - 30 Jun 2021
Cited by 4 | Viewed by 730
Abstract
Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on [...] Read more.
Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level. Full article
(This article belongs to the Special Issue Digital Transformation in the Agriculture Sector)
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