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

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 March 2021) | Viewed by 51522

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Special Issue Editors

Assoc. Prof. Sigfredo Fuentes

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Guest Editor

School of Agriculture and Food Sciences, University of Melbourne, Australia; Faculty of Veterinary and Agricultural Sciences, Parkville, Australia
Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Special Issues, Collections and Topics in MDPI journals

Dr. Ranjith R. Unnithan

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Guest Editor

Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC, Australia
Interests: sensor engineering; spanning image and spectral sensors; machine learning on sensors; augmented reality (AR) displays; sensors for space; drone-based sensor applications; electronic sensors for biomedical applications; new thermal spectral cameras and nanophotonic engineering
Special Issues, Collections and Topics in MDPI journals

Dr. Eden Tongson

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Guest Editor

School of Agriculture and Food Sciences, University of Melbourne, Australia; Faculty of Veterinary and Agricultural Sciences, Parkville, Australia
Interests: plant physiology; crop molecular genetics and biotechnology; computer vision; short-range remote sensing

Dr. Nir Lipovetzky

E-Mail Website
Guest Editor

Computing and Information Systems, The University of Melbourne, Melbourne, Australia
Interests: artificial intelligence; planning and search; autonomous systems; machine learning

Special Issue Information

Dear Colleagues,

Forecasted climate change scenarios are projecting an increase in climatic anomalies’ occurrence, severity, and intensity. Evidence of such anomalies has been reported worldwide, such as extreme heat waves, bushfires, severe frosts, dust storms, hailstorms, and catastrophic events, such as hurricanes, and heavy rains as part of monsoons or El Nino–Southern Oscillation (ENSO). These events have a direct impact on agricultural systems, urban green infrastructures, and forestry, which may be difficult to monitor due to large extensions, and complexity of the physiological and morphological architecture of subjects (i.e., plants, trees, animals). Therefore, implementation of new and emerging technologies could be an important tool to assess the effects of these environmental risks on important aspects of health and productivity that are relevant for the different areas mentioned. This Special Issue call is mainly focused on the integration of different sensors, remote sensing, and sensor network technologies with powerful data analysis techniques, such as machine learning and artificial intelligence. Priority will be given to those papers that integrate more than one new and emerging technology with AI analysis, interpretation, and decision-making systems to produce practical tools for growers, practitioners, urban planners, and the public in general. The latter is in the form of robust AI models, computer algorithms, applications, and software coupled with integrated hardware systems and connectivity (IoT).

The environmental hazards to be considered in this Special Issue call include but are not limited to:

Heat waves;
Hailstorms;
Frost events;
Bushfire events (fire and smoke);
Extreme rains;
Environmental pollution;
Dust pollution;
Contaminants (air, water, chemical).

The systems to be considered are:

Agricultural crops (broadacre, horticultural, glasshouse);
Animal production (effects on productivity, welfare and physiology);
Urban green infrastructure;
Forestry systems (natural and productive).

The sensors to be considered, but not limited to:

Electrical and optical sensors;
Infrared sensors;
Image sensors;
Gas sensors;
Radar;
Lidar;
Multispectral sensors;
General sensors (temperature, flow, moisture, humidity, pressure);
Ultrasonic sensors.

Assoc. Prof. Sigfredo Fuentes
Dr. Ranjith R Unnithan
Dr. Eden Tongson
Dr. Nir Lipovetzky
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. Sensors 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 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

environmental risks
artificial intelligence
remote sensing
sensor networks
Internet of Things
integrated sensors
computer vision

Published Papers (11 papers)

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Editorial

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3 pages, 167 KiB

Open AccessEditorial

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

by Sigfredo Fuentes and Eden Jane Tongson

Sensors 2021, 21(19), 6383; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196383 - 24 Sep 2021

Cited by 2 | Viewed by 1824

Abstract

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 [...] Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

Research

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22 pages, 4387 KiB

Open AccessArticle

Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling

by Sigfredo Fuentes, Eden Tongson, Ranjith R. Unnithan and Claudia Gonzalez Viejo

Sensors 2021, 21(17), 5948; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175948 - 04 Sep 2021

Cited by 30 | Viewed by 5027

Abstract

Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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16 pages, 6945 KiB

Open AccessArticle

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

by Sigfredo Fuentes, Eden Tongson and Claudia Gonzalez Viejo

Sensors 2021, 21(1), 295; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010295 - 04 Jan 2021

Cited by 11 | Viewed by 4255

Abstract

Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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15 pages, 1472 KiB

Open AccessArticle

Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network

by Ivana Lučin, Luka Grbčić, Zoran Čarija and Lado Kranjčević

Sensors 2021, 21(1), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010245 - 01 Jan 2021

Cited by 11 | Viewed by 3163

Abstract

In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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27 pages, 12128 KiB

Open AccessArticle

Extraction of Land Information, Future Landscape Changes and Seismic Hazard Assessment: A Case Study of Tabriz, Iran

by Ayub Mohammadi, Sadra Karimzadeh, Khalil Valizadeh Kamran and Masashi Matsuoka

Sensors 2020, 20(24), 7010; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247010 - 08 Dec 2020

Cited by 11 | Viewed by 2863

Abstract

Exact land cover inventory data should be extracted for future landscape prediction and seismic hazard assessment. This paper presents a comprehensive study towards the sustainable development of Tabriz City (NW Iran) including land cover change detection, future potential landscape, seismic hazard assessment and municipal performance evaluation. Landsat data using maximum likelihood (ML) and Markov chain algorithms were used to evaluate changes in land cover in the study area. The urbanization pattern taking place in the city was also studied via synthetic aperture radar (SAR) data of Sentinel-1 ground range detected (GRD) and single look complex (SLC). The age of buildings was extracted by using built-up areas of all classified maps. The logistic regression (LR) model was used for creating a seismic hazard assessment map. From the results, it can be concluded that the land cover (especially built-up areas) has seen considerable changes from 1989 to 2020. The overall accuracy (OA) values of the produced maps for the years 1989, 2005, 2011 and 2020 are 96%, 96%, 93% and 94%, respectively. The future potential landscape of the city showed that the land cover prediction by using the Markov chain model provided a promising finding. Four images of 1989, 2005, 2011 and 2020, were employed for built-up areas’ land information trends, from which it was indicated that most of the built-up areas had been constructed before 2011. The seismic hazard assessment map indicated that municipal zones of 1 and 9 were the least susceptible areas to an earthquake; conversely, municipal zones of 4, 6, 7 and 8 were located in the most susceptible regions to an earthquake in the future. More findings showed that municipal zones 1 and 4 demonstrated the best and worst performance among all zones, respectively. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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18 pages, 3554 KiB

Open AccessArticle

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

by Sigfredo Fuentes, Claudia Gonzalez Viejo, Surinder S. Chauhan, Aleena Joy, Eden Tongson and Frank R. Dunshea

Sensors 2020, 20(21), 6334; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216334 - 06 Nov 2020

Cited by 16 | Viewed by 4273

Abstract

Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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15 pages, 1793 KiB

Open AccessEditor’s ChoiceArticle

Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach

by Sigfredo Fuentes, Vasiliki Summerson, Claudia Gonzalez Viejo, Eden Tongson, Nir Lipovetzky, Kerry L. Wilkinson, Colleen Szeto and Ranjith R. Unnithan

Sensors 2020, 20(18), 5108; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185108 - 08 Sep 2020

Cited by 34 | Viewed by 4018

Abstract

Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R² = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R² = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R² = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R² = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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24 pages, 2697 KiB

Open AccessArticle

Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

by Vasiliki Summerson, Claudia Gonzalez Viejo, Colleen Szeto, Kerry L. Wilkinson, Damir D. Torrico, Alexis Pang, Roberta De Bei and Sigfredo Fuentes

Sensors 2020, 20(18), 5099; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185099 - 07 Sep 2020

Cited by 11 | Viewed by 3487

Abstract

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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11 pages, 1906 KiB

Open AccessArticle

Artificial Intelligence Applied to a Robotic Dairy Farm to Model Milk Productivity and Quality based on Cow Data and Daily Environmental Parameters

by Sigfredo Fuentes, Claudia Gonzalez Viejo, Brendan Cullen, Eden Tongson, Surinder S. Chauhan and Frank R. Dunshea

Sensors 2020, 20(10), 2975; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102975 - 24 May 2020

Cited by 34 | Viewed by 8332

Abstract

Increased global temperatures and climatic anomalies, such as heatwaves, as a product of climate change, are impacting the heat stress levels of farm animals. These impacts could have detrimental effects on the milk quality and productivity of dairy cows. This research used four years of data from a robotic dairy farm from 36 cows with similar heat tolerance (Model 1), and all 312 cows from the farm (Model 2). These data consisted of programmed concentrate feed and weight combined with weather parameters to develop supervised machine learning fitting models to predict milk yield, fat and protein content, and actual cow concentrate feed intake. Results showed highly accurate models, which were developed for cows with a similar genetic heat tolerance (Model 1: n = 116, 456; R = 0.87; slope = 0.76) and for all cows (Model 2: n = 665, 836; R = 0.86; slope = 0.74). Furthermore, an artificial intelligence (AI) system was proposed to increase or maintain a targeted level of milk quality by reducing heat stress that could be applied to a conventional dairy farm with minimal technology addition. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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17 pages, 724 KiB

Open AccessArticle

A Machine Learning-based Algorithm for Water Network Contamination Source Localization

by Luka Grbčić, Ivana Lučin, Lado Kranjčević and Siniša Družeta

Sensors 2020, 20(9), 2613; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092613 - 03 May 2020

Cited by 15 | Viewed by 3482

Abstract

In this paper, a novel machine learning based algorithm for water supply pollution source identification is presented built specifically for high performance parallel systems. The algorithm utilizes the combination of Artificial Neural Networks for classification of the pollution source with Random Forests for regression analysis to determine significant variables of a contamination event such as start time, end time and contaminant chemical concentration. The algorithm is based on performing Monte Carlo water quality and hydraulic simulations in parallel, recording data with sensors placed within a water supply network and selecting a most probable pollution source based on a tournament style selection between suspect nodes in a network with mentioned machine learning methods. The novel algorithmic framework is tested on a small (92 nodes) and medium sized (865 nodes) water supply sensor network benchmarks with a set contamination event start time, end time and chemical concentration. Out of the 30 runs, the true source node was the finalist of the algorithm’s tournament style selection for 30/30 runs for the small network, and 29/30 runs for the medium sized network. For all the 30 runs on the small sensor network, the true contamination event scenario start time, end time and chemical concentration was set as 14:20, 20:20 and 813.7 mg/L, respectively. The root mean square errors for all 30 algorithm runs for the three variables were 48 min, 4.38 min and 18.06 mg/L. For the 29 successful medium sized network runs the start time was 06:50, end time 07:40 and chemical concentration of 837 mg/L and the root mean square errors were 6.06 min, 12.36 min and 299.84 mg/L. The algorithmic framework successfully narrows down the potential sources of contamination leading to a pollution source identification, start and ending time of the event and the contaminant chemical concentration. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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Review

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22 pages, 434 KiB

Open AccessReview

Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions

by Adegbite Adesipo, Oluwaseun Fadeyi, Kamil Kuca, Ondrej Krejcar, Petra Maresova, Ali Selamat and Mayowa Adenola

Sensors 2020, 20(21), 5977; https://0-doi-org.brum.beds.ac.uk/10.3390/s20215977 - 22 Oct 2020

Cited by 39 | Viewed by 7396

Abstract

Attention has shifted to the development of villages in Europe and other parts of the world with the goal of combating rural–urban migration, and moving toward self-sufficiency in rural areas. This situation has birthed the smart village idea. Smart village initiatives such as those of the European Union is motivating global efforts aimed at improving the live and livelihood of rural dwellers. These initiatives are focused on improving agricultural productivity, among other things, since most of the food we eat are grown in rural areas around the world. Nevertheless, a major challenge faced by proponents of the smart village concept is how to provide a framework for the development of the term, so that this development is tailored towards sustainability. The current work examines the level of progress of climate smart agriculture, and tries to borrow from its ideals, to develop a framework for smart village development. Given the advances in technology, agricultural development that encompasses reduction of farming losses, optimization of agricultural processes for increased yield, as well as prevention, monitoring, and early detection of plant and animal diseases, has now embraced varieties of smart sensor technologies. The implication is that the studies and results generated around the concept of climate smart agriculture can be adopted in planning of villages, and transforming them into smart villages. Hence, we argue that for effective development of the smart village framework, smart agricultural techniques must be prioritized, viz-a-viz other developmental practicalities. Full article

(This article belongs to the Special Issue Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems)

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