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Editorial: Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems

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Abstract

Artificial intelligence (AI), together with robotics, sensors, sensor networks, internet of things (IoT) and machine/deep learning modeling have reached the forefront to-wards the goal of increased efficiency in multitude of application and purpose. The development and application of AI requires specific considerations, approaches, and methodologies. This special issue focused on the applications of AI to environmental systems related to hazard assessment in Urban, Agriculture and Forestry. A total of ten papers were published in this special issue, with topics ranging from reviewing the current climate-smart agriculture approaches for smart village development to the integration of visible and infrared thermal cameras for automated urban green infra-structure monitoring on top of moving vehicles; implementation of machine learn-ing to classify contaminant sources for urban water networks; water network con-tamination assessment using machine learning in the UK; future landscape changes, seismic and hazard assessment tested in Tabriz, Iran assessed using satellite remote sensing; AI applied to a robotic dairy farm to assess milk productivity and quality traits using meteorological and cow data; AI and computer vision from visible and infrared thermal images to obtain non-invasive biometrics from sheep to assess welfare; assessment of smoke contamination and smoke taint in wines due to bushfires us-ing a low-cost electronic nose and AI; classification of smoke contaminated grape-vine berries and leaves using chemical fingerprinting and machine learning; and detection of aphid infestation in wheat plants and insect-plant physiological interac-tions using low-cost electronic noses, chemical fingerprinting and machine learing.
sensors
Editorial
Editorial: Special Issue “Implementation of Sensors and
Artificial Intelligence for Environmental Hazards Assessment
in Urban, Agriculture and Forestry Systems”
Sigfredo Fuentes * and Eden Jane Tongson


Citation: Fuentes, S.; Tongson, E.J.
Editorial: Special Issue
“Implementation of Sensors and
Artificial Intelligence for
Environmental Hazards Assessment
in Urban, Agriculture and Forestry
Systems”. Sensors 2021,21, 6383.
https://doi.org/10.3390/s21196383
Received: 14 September 2021
Accepted: 18 September 2021
Published: 24 September 2021
Publisher’s Note: MDPI stays neutral
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iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Digital Agriculture, Food and Wine Research Group, Faculty of Veterinary and Agricultural Sciences,
School of Agriculture and Food, The University of Melbourne, Parkville, VIC 3010, Australia;
eden.tongson@unimelb.edu.au
*Correspondence: sfuentes@unimelb.edu.au
Artificial intelligence (AI), together with robotics, sensors, sensor networks, internet of
things (IoT) and machine/deep learning modeling, has reached the forefront towards the
goal of increased efficiency in a multitude of application and purpose. The development
and application of AI requires specific considerations, approaches, and methodologies. This
special issue focused on the applications of AI to environmental systems related to hazard
assessment in Urban, Agriculture and Forestry. A total of ten papers were published in
this special issue, with topics ranging from reviewing the current climate-smart agriculture
approaches for smart village development [
1
] to the integration of visible and infrared
thermal cameras for automated urban green infrastructure monitoring on top of moving
vehicles [
2
]; the implementation of machine learning to classify contaminant sources
for urban water networks [
3
]; water network contamination assessment using machine
learning in the UK [
4
]; future landscape changes, seismic and hazard assessment tested in
Tabriz, Iran assessed using satellite remote sensing [
5
]; AI applied to a robotic dairy farm to
assess milk productivity and quality traits using meteorological and cow data [
6
]; AI and
computer vision from visible and infrared thermal images to obtain non-invasive biometrics
from sheep to assess welfare [
7
]; the assessment of smoke contamination and smoke taint
in wines due to bushfires using a low-cost electronic nose and AI [
8
]; the classification
of smoke contaminated grapevine berries and leaves using chemical fingerprinting and
machine learning [
9
]; and the detection of aphid infestation in wheat plants and insect-plant
physiological interactions using low-cost electronic noses, chemical fingerprinting and
machine learing [10].
The development of smart villages in Europe requires a framework to secure sus-
tainability based on climate-smart agriculture. As argued by Adesipo et al. [
1
], these
considerations need to be based on advances in technology to increase yield and minimize
the farming losses associated with biotic and abiotic stresses. This approach will help
for the efficient planning and management of smart villages with smart agriculture. The
proposed frameworks will secure the success of these smart-agriculture practices under
current and future climate change scenarios, making the system flexible and reactive based
on recent smart technological advances related to sensor technologies for automated moni-
toring, data processing and reporting. Digital technological advances were reported for
an automated urban green infrastructure monitoring using integrated visible and infrared
thermal cameras in Fuentes et al. [
2
]. Studied in Melbourne, Australia, this system is a novel
assessment method which utilizes moving vehicles as monitoring robots to assess tree by
tree growth and water status using computer vision algorithms. It was suggested that
this system could be used on public transport to support the city council’s management,
maintenance and improvement of green infrastructure and as a potential tool to increase
urban resiliency to climate change, specifically against the urban heat island effect.
Sensors 2021,21, 6383. https://doi.org/10.3390/s21196383 https://www.mdpi.com/journal/sensors
Sensors 2021,21, 6383 2 of 3
One of the detrimental effects of reduced green infrastructure is the contamination
of waterways and water networks. The study by Luˇcin et al. [
3
] proposed a classification
system based on machine learning models (Neural Network and Random Forest) to predict
the number of contaminant injections in the Richmond water supply, UK. This study also
proposed that the implementation of these algorithms can be used to run simulations to
detect potential contamination risks and nodes with a high probability of contamination,
making a management system more predictive than reactive with such vital urban re-
sources. Similar work was conducted by Grbˇci´c et al. [
4
] to locate contamination sources
in water networks with a combination of Artificial Neural Network (ANN) to classify
pollution sources. Other types of hazard assessments in urban systems were based on
a case study in Tabriz, Iran, by Mohammadi et al. [
5
]. Using satellite remote sensing to
extract land information made it possible to predict landscape changes due to seismic
activity with high accuracy ranging from 94 to 96%. These technological advances can be
extrapolated to other cities with similar risks.
For agricultural systems, novel digital technologies were applied for farm animal
welfare assessment based on weather information and cow data to predict milk productivity
and quality through supervised machine learning [
6
]. The models developed presented
high accuracies for correlation models in the range of R = 0.86 and R = 0.87, respectively.
The proposed AI system’s automation can be implemented in robotic and conventional
dairy farms to respond more efficiently to climatic anomalies, such as cold stress or heat
waves, to maintain animal welfare. Heat stress in animal transport has recently been a
focus of public concern due to the high mortality of animals transported by sea passing; for
example, the Persian Gulf with 50
C. A high level of heat stress can result in serious health
issues to animals and ultimately death. Digital and AI technologies based on integrated
visible and infrared thermal cameras were proposed by Fuentes et al. [
7
] to assess the
physiological parameters of sheep in heat stress environments. The proposed models
showed high accuracies to monitor the heart rate, respiration rate and skin temperature
of animals. These digital technologies could help farmers manage their livestock more
efficiently through objective assessments of animal welfare.
Climate change effects include the increased incidence, number and severity of climatic
anomalies such as heatwaves and bushfires. These climatic anomalies have a specific impact
on viticulture and winemaking, specifically with bushfires producing smoke contamination
on leaves and berries, which are later passed to the wine through the fermentation process.
These have been investigated in two studies focused on implementing digital technologies
and machine learning modeling using low-cost electronic noses [
8
] and near-infrared
spectroscopy to assess the levels of smoke contamination in berries and smoke taint in
wines [
9
]. The models demonstrated high accuracy, showing the good potential of these
approaches as practical options for grape-growers. The application of these tools offers
an accurate, cost-effective and objective assessment of smoke contamination and taint in
wines for efficient management purposes.
Finally, low-cost electronic noses and near-infrared spectroscopy were also imple-
mented to assess the infestation of insects in plants and the insect-plant interaction [
10
].
This study presented a novel way to sniff aphid infestation in wheat plants and estimate
plant physiological parameters using machine learning modeling. Models developed
resulted in the high accuracy of monitoring insect numbers, early infestation and physio-
logical parameters such as photosynthesis, transpiration and stomatal conductance, which
usually require expensive instrumentation for single leaf measurements. This research
also proposed a deployment system using unmanned aerial vehicles (UAV) to increase the
spatial and temporal monitoring scales for more efficient assessments.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Sensors 2021,21, 6383 3 of 3
Data Availability Statement: Data availability is specified for every published paper.
Conflicts of Interest: The authors declare no conflict of interest.
References
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Adesipo, A.; Fadeyi, O.; Kuca, K.; Krejcar, O.; Maresova, P.; Selamat, A.; Adenola, M. Smart and Climate-Smart Agricultural
Trends as Core Aspects of Smart Village Functions. Sensors 2020,20, 5977. [CrossRef] [PubMed]
2.
Fuentes, S.; Tongson, E.; Gonzalez Viejo, C. Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible
and Thermal Infrared Cameras Mounted on a Moving Vehicle. Sensors 2021,21, 295. [CrossRef] [PubMed]
3.
Luˇcin, I.; Grbˇci´c, L.; ˇ
Carija, Z.; Kranjˇcevi´c, L. Machine-learning classification of a number of contaminant sources in an urban
water network. Sensors 2021,21, 245. [CrossRef] [PubMed]
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Grbˇci´c, L.; Luˇcin, I.; Kranjˇcevi´c, L.; Družeta, S. A machine learning-based algorithm for water network contamination source
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Mohammadi, A.; Karimzadeh, S.; Valizadeh Kamran, K.; Matsuoka, M. Extraction of land information, future landscape changes
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Fuentes, S.; Gonzalez Viejo, C.; Cullen, B.; Tongson, E.; Chauhan, S.S.; Dunshea, F.R. Artificial Intelligence Applied to a Robotic
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Fuentes, S.; Gonzalez Viejo, C.; Chauhan, S.S.; Joy, A.; Tongson, E.; Dunshea, F.R. Non-Invasive Sheep Biometrics Obtained by
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Summerson, V.; Gonzalez Viejo, C.; Szeto, C.; Wilkinson, K.L.; Torrico, D.D.; Pang, A.; De Bei, R.; Fuentes, S. Classification of
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Fuentes, S.; Tongson, E.; Unnithan, R.R.; Gonzalez Viejo, C. Early Detection of Aphid Infestation and Insect-Plant Interaction
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