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Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture
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Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 30146

Special Issue Editors

Prof. Dr. Thomas Bartzanas

E-Mail Website
Guest Editor
Department of Natural Resources Management and Agricultural Engineering (NRM&AE), Agricultural University of Athens, 11855 Athens, Greece
Interests: greenhouse; hydroponics; vertical farming; environmental control; environmental impact assessment; CFD
Special Issues, Collections and Topics in MDPI journals
Dr. Tomas Norton

E-Mail Website
Guest Editor
Measure, Model, Manage Bio-Responses (M3-BIORES), Animal & Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
Interests: precision livestock farming (PLF); modelling and management of animal responses; animal health and welfare
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Enhancing the EU's resilience to climate change and food security requires investment in a low-carbon economy that promotes energy efficiency and the uptake of green products. The recent proliferation of technical innovations in agriculture and emergence of the Agri-Tech Industry demonstrates how socio-economic benefits to EU citizens can be derived from tackling food production problems linked with greenhouse gas emissions, over consumption of energy and water. One such issue is search for novel approaches for indoor food production to sustainably feed highly urbanized regions. The presents new challenges to the field of Controlled Environment Agriculture (CEA), and makes it ripe for new thinking to underpin innovations required to meet emerging indoor food production techniques that are a necessary part a resilient EU agricultural sector. CEA is "an integrated science- and engineering-based approach to provide specific environments for plant and animals productivity while optimizing resources including water, energy, space, capital and labour. A more efficient plant and animal production is only possible when improving the control of the processes and factors involved.

This Special Issue aims to discuss various sustainability issues related to controlled environment agriculture. This will include:

  • the development and implementation of precision agriculture technologies and techniques for animal or crop health or welfare monitoring,
  • novel structures and systems design for energy efficiency and emissions reduction,
  • improved control of the animal/crop micro-environment,
  • development and testing of new materials and novel modelling aspects.

We invite you to contribute to this issue by submitting original research papers along these topics, comprehensive reviews and specific case studies that focus on methods, models, techniques and analyses applied to controlled environment agricultural systems. 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.

Prof. Dr. Thomas Bartzanas
Prof. Dr. Tomas Norton
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. Sustainability 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

  • greenhouse
  • livestock buildings
  • vertical farming
  • micro-environment
  • environmental assessment
  • automation
  • control
  • ICT
  • decision support systems

Published Papers (6 papers)

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Research

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18 pages, 5053 KiB  
Article
Deep Learning Predictor for Sustainable Precision Agriculture Based on Internet of Things System
by Xue-Bo Jin, Xing-Hong Yu, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su and Jian-Lei Kong
Sustainability 2020, 12(4), 1433; https://doi.org/10.3390/su12041433 - 14 Feb 2020
Cited by 75 | Viewed by 5942
Abstract
Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict [...] Read more.
Based on the collected weather data from the agricultural Internet of Things (IoT) system, changes in the weather can be obtained in advance, which is an effective way to plan and control sustainable agricultural production. However, it is not easy to accurately predict the future trend because the data always contain complex nonlinear relationship with multiple components. To increase the prediction performance of the weather data in the precision agriculture IoT system, this study used a deep learning predictor with sequential two-level decomposition structure, in which the weather data were decomposed into four components serially, then the gated recurrent unit (GRU) networks were trained as the sub-predictors for each component. Finally, the results from GRUs were combined to obtain the medium- and long-term prediction result. The experiments were verified for the proposed model based on weather data from the IoT system in Ningxia, China, for wolfberry planting, in which the prediction results showed that the proposed predictor can obtain the accurate prediction of temperature and humidity and meet the needs of precision agricultural production. Full article
(This article belongs to the Special Issue Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture)
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15 pages, 3699 KiB  
Article
A Data-Based Fault-Detection Model for Wireless Sensor Networks
by Jangsik Bae, Meonghun Lee and Changsun Shin
Sustainability 2019, 11(21), 6171; https://doi.org/10.3390/su11216171 - 05 Nov 2019
Cited by 15 | Viewed by 3196
Abstract
With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the [...] Read more.
With the expansion of smart agriculture, wireless sensor networks are being increasingly applied. These networks collect environmental information, such as temperature, humidity, and CO2 rates. However, if a faulty sensor node operates continuously in the network, unnecessary data transmission adversely impacts the network. Accordingly, a data-based fault-detection algorithm was implemented in this study to analyze data of sensor nodes and determine faults, to prevent the corresponding nodes from transmitting data; thus, minimizing damage to the network. A cloud-based “farm as a service” optimized for smart farms was implemented as an example, and resource management of sensors and actuators was provided using the oneM2M common platform. The effectiveness of the proposed fault-detection model was verified on an integrated management platform based on the Internet of Things by collecting and analyzing data. The results confirm that when a faulty sensor node is not separated from the network, unnecessary data transmission of other sensor nodes occurs due to continuous abnormal data transmission; thus, increasing energy consumption and reducing the network lifetime. Full article
(This article belongs to the Special Issue Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture)
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17 pages, 12712 KiB  
Article
Augmented Reality in the Integrative Internet of Things (AR-IoT): Application for Precision Farming
by Pilaiwan Phupattanasilp and Sheau-Ru Tong
Sustainability 2019, 11(9), 2658; https://doi.org/10.3390/su11092658 - 09 May 2019
Cited by 76 | Viewed by 8128
Abstract
Benefitted by the Internet of Things (IoT), visualization capabilities facilitate the improvement of precision farming, especially in dynamic indoor planting. However, conventional IoT data visualization is usually carried out in offsite and textual environments, i.e., text and number, which do not promote a [...] Read more.
Benefitted by the Internet of Things (IoT), visualization capabilities facilitate the improvement of precision farming, especially in dynamic indoor planting. However, conventional IoT data visualization is usually carried out in offsite and textual environments, i.e., text and number, which do not promote a user’s sensorial perception and interaction. This paper introduces the use of augmented reality (AR) as a support to IoT data visualization, called AR-IoT. The AR-IoT system superimposes IoT data directly onto real-world objects and enhances object interaction. As a case study, this system is applied to crop monitoring. Multi-camera, a non-destructive and low-cost imaging platform of the IoT, is connected to the internet and integrated into the system to measure the three-dimensional (3D) coordinates of objects. The relationships among accuracy, object coordinates, augmented information (e.g., virtual objects), and object interaction are investigated. The proposed system shows a great potential to integrate IoT data with AR resolution, which will effectively contribute to updating precision agricultural techniques in an environmentally sustainable manner. Full article
(This article belongs to the Special Issue Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture)
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18 pages, 3086 KiB  
Article
Adapting Models to Warn Fungal Diseases in Vineyards Using In-Field Internet of Things (IoT) Nodes
by Sergio Trilles Oliver, Alberto González-Pérez and Joaquín Huerta Guijarro
Sustainability 2019, 11(2), 416; https://doi.org/10.3390/su11020416 - 15 Jan 2019
Cited by 19 | Viewed by 3820
Abstract
Weather conditions are one of the main threats that can lead to diseases in crops. Unfavourable conditions, such as rain or high humidity, can produce a risk of fungal diseases. Meteorological monitoring is vital to have some indication of a possible infection. The [...] Read more.
Weather conditions are one of the main threats that can lead to diseases in crops. Unfavourable conditions, such as rain or high humidity, can produce a risk of fungal diseases. Meteorological monitoring is vital to have some indication of a possible infection. The literature contains a wide variety of models for warning for this type of disease.These are capable of warning when an infection may be present. Devices (weather stations) able to measure weather conditions in real-time are needed to know precisely when an infection occurs in a smallholding. Besides, such models cannot be executed at the same time in which the observations are collected; in fact, these models are usually executed in batches at a rate of one per day. Therefore, these models need to be adapted to run at the same frequency as that at which observations are collected so that a possible disease can be dealt with as early as possible. The primary aim of this work is to adapt disease warning models to run in (near) real-time over meteorological variables generated by Internet of Things (IoT) devices, in order to inform farmers as quickly as possible if their crop is in danger of being infected by diseases, and to enable them to tackle the infection with the appropriate treatments. The work is centered on vineyards and has been tested in four different smallholdings in the province of Castellón (Spain). Full article
(This article belongs to the Special Issue Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture)
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Review

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21 pages, 1577 KiB  
Review
Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®
by Mona L. V. Larsen, Meiqing Wang and Tomas Norton
Sustainability 2021, 13(2), 692; https://doi.org/10.3390/su13020692 - 12 Jan 2021
Cited by 40 | Viewed by 4778
Abstract
The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, [...] Read more.
The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, only evaluates a subset of the animals, and is performed by the subjective human. Automation of the assessment through information technologies (ITs) could provide a continuous objective assessment in real-time on all animals. The aim of the current systematic review was to identify ITs developed for welfare monitoring within the pig production chain, evaluate the ITs developmental stage and evaluate how these ITs can be related to the WQ assessment protocol. The systematic literature search identified 101 publications investigating the development of ITs for welfare monitoring within the pig production chain. The systematic literature analysis revealed that the research field is still young with 97% being published within the last 20 years, and still growing with 63% being published between 2016 and mid-2020. In addition, most focus is still on the development of ITs (sensors) for the extraction and analysis of variables related to pig welfare; this being the first step in the development of a precision livestock farming system for welfare monitoring. The majority of the studies have used sensor technologies detached from the animals such as cameras and microphones, and most investigated animal biomarkers over environmental biomarkers with a clear focus on behavioural biomarkers over physiological biomarkers. ITs intended for many different welfare issues have been studied, although a high number of publications did not specify a welfare issue and instead studied a general biomarker such as activity, feeding behaviour and drinking behaviour. The ‘good feeding’ principle of the WQ assessment protocol was the best represented with ITs for real-time on-farm welfare assessment, while for the other principles only few of the included WQ measures are so far covered. No ITs have yet been developed for the ‘Comfort around resting’ and the ‘Good human-animal relationship’ criteria. Thus, the potential to develop ITs for welfare assessment within the pig production is high and much work is still needed to end up with a remote solution for welfare assessment on-farm and in real-time. Full article
(This article belongs to the Special Issue Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture)
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34 pages, 970 KiB  
Review
How Are Information Technologies Addressing Broiler Welfare? A Systematic Review Based on the Welfare Quality® Assessment
by Heitor Vieira Rios, Paulo Dabdab Waquil, Patrícia Soster de Carvalho and Tomas Norton
Sustainability 2020, 12(4), 1413; https://doi.org/10.3390/su12041413 - 14 Feb 2020
Cited by 18 | Viewed by 3171
Abstract
This systematic review aims to explore how information technologies (ITs) are currently used to monitor the welfare of broiler chickens. The question posed for the review was “which ITs are related to welfare and how do they monitor this for broilers?”. The Welfare [...] Read more.
This systematic review aims to explore how information technologies (ITs) are currently used to monitor the welfare of broiler chickens. The question posed for the review was “which ITs are related to welfare and how do they monitor this for broilers?”. The Welfare Quality® (WQ) protocol for broiler assessment was utilized as a framework to analyse suitable articles. A total of 57 studies were reviewed wherein all principles of broiler welfare were addressed. The “good health” principle was the main criteria found to be addressed by ITs and IT-based studies (45.6% and 46.1%, respectively), whereas the least observed principle was “good feeding” (8.8%). This review also classified ITs and IT-based studies by their utilization (location, production system, variable measured, aspect of production, and experimental/practical use). The results show that the current focus of ITs is on problems with conventional production systems and that less attention has been given to free-range systems, slaughterhouses, and supply chain issues. Given the valuable results evidenced by the exploitation of ITs, their use in broiler production should continue to be encouraged with more attention given to farmer adoption strategies. Full article
(This article belongs to the Special Issue Precision Agricultural Technologies for Sustainable Controlled Environment Agriculture)
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