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Emerging Sensors Techniques and Technologies for Intelligent Environments

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

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 54718

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Special Issue Editors

Dr. Ionut Anghel

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Guest Editor

Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu Street, Cluj-Napoca, Romania
Interests: ambient assistive living; adaptive systems; blockchain; decentralized distributed systems
Special Issues, Collections and Topics in MDPI journals

Dr. Tudor Cioara

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Guest Editor

Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu street, Cluj-Napoca, Romania
Interests: blockchain; smart environments; complex distributed systems; machine learning; energy efficiency and smart grid
Special Issues, Collections and Topics in MDPI journals

Dr. Marcel Antal

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Guest Editor

Researcher at Distributed Systems Research Laboratory; Senior lecturer at Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu Street, Cluj-Napoca, Romania
Interests: IoT; blockchain; big data analytics; multidisciplinary optimization; complex systems modelling; smart energy grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The trending techniques, models, algorithms, and technologies for managing indoor and outdoor intelligent environments rely heavily on data acquisition through a diversity of heterogeneous Internet of Things (IoT) devices and sensors. They process and analyze large and heterogeneous streams of data with the general objective of extracting knowledge and making reinforced decisions for adapting to complex events from such environments.
In this context, innovative services supported by sensor-based monitoring can be developed, enabling the intelligent management of smart cities, smart grids, ambient assisted living, factories of the future, logistics chains, etc. These services can also benefit from the emerging promising technologies that can provide the required infrastructure for adding the envisioned intelligence: cloud-based models, deep learning, fog, and edge computing, blockchain, and digital twins.
This Special Issue offers opportunities for publishing innovative solutions, techniques, and technologies for the development and management of intelligent environments. Potential interesting topics for this Special Issue include, but are not limited to, the following:

IoT-based indoor or outdoor smart environments;
Blockchain-enabled solutions for smart cities and smart grids;
Smart grid monitoring, management, and optimization;
Fog and edge computing in the IoT;
Technologies for age-friendly cities;
Ambient assisted living systems;
Solutions for industrial IoT and factories of the future;
Big data and machine learning techniques;
Intelligent transport systems and logistic chains;
Intelligent healthcare systems;
Local energy communities and demand response.

Dr. Ionut Anghel
Dr. Tudor Cioara
Dr. Marcel Antal
Guest Editors

Manuscript Submission Information

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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.

Published Papers (14 papers)

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Editorial

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3 pages, 183 KiB

Open AccessEditorial

Emerging Sensors Techniques and Technologies for Intelligent Environments

by Ionut Anghel and Tudor Cioara

Sensors 2022, 22(17), 6427; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176427 - 26 Aug 2022

Viewed by 1061

Abstract

The trending techniques for managing indoor and outdoor intelligent environments rely heavily on data acquisition through a diversity of heterogeneous Internet of Things (IoT) devices and sensors [...] Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

Research

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33 pages, 19332 KiB

Open AccessEditor’s ChoiceArticle

Smart Strawberry Farming Using Edge Computing and IoT

by Mateus Cruz, Samuel Mafra, Eduardo Teixeira and Felipe Figueiredo

Sensors 2022, 22(15), 5866; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155866 - 05 Aug 2022

Cited by 20 | Viewed by 5292

Abstract

Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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20 pages, 3405 KiB

Open AccessArticle

Identifying and Monitoring the Daily Routine of Seniors Living at Home

by Viorica Rozina Chifu, Cristina Bianca Pop, David Demjen, Radu Socaci, Daniel Todea, Marcel Antal, Tudor Cioara, Ionut Anghel and Claudia Antal

Sensors 2022, 22(3), 992; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030992 - 27 Jan 2022

Cited by 14 | Viewed by 4988

Abstract

As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers’ interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monitoring the activities of older adults. The results are promising, the proposed techniques can identify the daily routines with confidence concerning the activity duration of 0.98 and the sequence of activities in the interval of [0.0794, 0.0829]. Regarding deviation identification, our method obtains 0.88 as the best sensitivity value with an average precision of 0.95. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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16 pages, 735 KiB

Open AccessArticle

Data-Driven Performance Evaluation Framework for Multi-Modal Public Transport Systems

by Ana Belén Rodríguez González, Juan José Vinagre Díaz, Mark R. Wilby and Rubén Fernández Pozo

Sensors 2022, 22(1), 17; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010017 - 21 Dec 2021

Cited by 4 | Viewed by 2716

Abstract

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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20 pages, 414 KiB

Open AccessArticle

Security Management Suitable for Lifecycle of Personal Information in Multi-User IoT Environment

by Yong Lee and Goo Yeon Lee

Sensors 2021, 21(22), 7592; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227592 - 16 Nov 2021

Cited by 4 | Viewed by 1730

Abstract

In recent years, as all actions of Internet users become information, the importance of personal information is emphasized, but in reality, the management of personal information is still insufficient. With the advent of the concept of sharing systems such as the sharing economy, the numbers of IoT application services (for example, a healthcare service using sharing IoT devices, or a vehicle sharing system with IoT devices) using users’ personal information are increasing, but the risk of using personal information is not managed. To solve this issue, the European GDPR stipulates the content of personal information protection. In this paper, we present a method to securely manage personal information in IoT devices in IoT application environments in accordance with the GDPR. We first describe the lifecycle stages of personal information occurring in IoT application services and propose a method to securely manage personal information at each stage of the lifecycle according to the flow of personal information in IoT devices. We also evaluated the usefulness and applicability of the proposed scheme through two service scenarios. Since the proposed method satisfies the requirements for personal information management in IoT application environments, it is expected to contribute to the development of the IoT business field that handles personal information. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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22 pages, 11779 KiB

Open AccessArticle

Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities

by Cicerone Laurentiu Popa, Tiberiu Gabriel Dobrescu, Catalin-Ionut Silvestru, Alexandru-Cristian Firulescu, Constantin Adrian Popescu and Costel Emil Cotet

Sensors 2021, 21(21), 7329; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217329 - 03 Nov 2021

Cited by 10 | Viewed by 2597

Abstract

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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27 pages, 8496 KiB

Open AccessArticle

Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

by Dacian I. Jurj, Levente Czumbil, Bogdan Bârgăuan, Andrei Ceclan, Alexis Polycarpou and Dan D. Micu

Sensors 2021, 21(9), 2946; https://0-doi-org.brum.beds.ac.uk/10.3390/s21092946 - 22 Apr 2021

Cited by 8 | Viewed by 2752

Abstract

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO₂ emissions reduction) or the resulted values were far much higher (>50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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22 pages, 2925 KiB

Open AccessArticle

Heating Homes with Servers: Workload Scheduling for Heat Reuse in Distributed Data Centers

by Marcel Antal, Andrei-Alexandru Cristea, Victor-Alexandru Pădurean, Tudor Cioara, Ionut Anghel, Claudia Antal (Pop), Ioan Salomie and Nicolas Saintherant

Sensors 2021, 21(8), 2879; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082879 - 20 Apr 2021

Cited by 4 | Viewed by 3093

Abstract

Data centers consume lots of energy to execute their computational workload and generate heat that is mostly wasted. In this paper, we address this problem by considering heat reuse in the case of a distributed data center that features IT equipment (i.e., servers) installed in residential homes to be used as a primary source of heat. We propose a workload scheduling solution for distributed data centers based on a constraint satisfaction model to optimally allocate workload on servers to reach and maintain the desired home temperature setpoint by reusing residual heat. We have defined two models to correlate the heat demand with the amount of workload to be executed by the servers: a mathematical model derived from thermodynamic laws calibrated with monitored data and a machine learning model able to predict the amount of workload to be executed by a server to reach a desired ambient temperature setpoint. The proposed solution was validated using the monitored data of an operational distributed data center. The server heat and power demand mathematical model achieve a correlation accuracy of 11.98% while in the case of machine learning models, the best correlation accuracy of 4.74% is obtained for a Gradient Boosting Regressor algorithm. Also, our solution manages to distribute the workload so that the temperature setpoint is met in a reasonable time, while the server power demand is accurately following the heat demand. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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18 pages, 7192 KiB

Open AccessArticle

Design, Implementation, and Configuration of Laser Systems for Vehicle Detection and Classification in Real Time

by Nieves Gallego Ripoll, Luís Enrique Gómez Aguilera, Ferran Mocholí Belenguer, Antonio Mocholí Salcedo and Francisco José Ballester Merelo

Sensors 2021, 21(6), 2082; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062082 - 16 Mar 2021

Cited by 3 | Viewed by 2492

Abstract

The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of sensors do not offer all the information currently required for exhaustive and comprehensive traffic control. Thus, this paper presents the design, implementation, and configuration of laser systems to obtain 3D profiles of vehicles, which collect more precise information about the state of the roads. Nevertheless, to obtain reliable information on vehicle traffic by means of these systems, it is fundamental to correctly carry out a series of preliminary steps: choose the most suitable type of laser, select its configuration properly, determine the optimal location, and process the information provided accurately. Therefore, this paper details a series of criteria to help make these crucial and difficult decisions. Furthermore, following these guidelines, a complete laser system implemented for vehicle detection and classification is presented as result, which is characterized by its versatility and the ability to control up to four lanes in real time. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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15 pages, 3574 KiB

Open AccessCommunication

Large-Scale Water Quality Prediction Using Federated Sensing and Learning: A Case Study with Real-World Sensing Big-Data

by Soohyun Park, Soyi Jung, Haemin Lee, Joongheon Kim and Jae-Hyun Kim

Sensors 2021, 21(4), 1462; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041462 - 20 Feb 2021

Cited by 16 | Viewed by 2727

Abstract

Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, a new system and algorithm is required to predict the green tide phenomenon and also minimize the related damage before the green tide occurs. For this purpose, we consider a new network model using smart sensor-based federated learning which is able to use distributed observation data with geologically separated local models. Moreover, we design an optimal scheduler which is beneficial to use real-time big data arrivals to make the overall network system efficient. The proposed scheduling algorithm is effective in terms of (1) data usage and (2) the performance of green tide occurrence prediction models. The advantages of the proposed algorithm is verified via data-intensive experiments with real water quality big-data. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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28 pages, 2540 KiB

Open AccessArticle

Intent Detection and Slot Filling with Capsule Net Architectures for a Romanian Home Assistant

by Anda Stoica, Tibor Kadar, Camelia Lemnaru, Rodica Potolea and Mihaela Dînşoreanu

Sensors 2021, 21(4), 1230; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041230 - 09 Feb 2021

Cited by 6 | Viewed by 3575

Abstract

As virtual home assistants are becoming more popular, there is an emerging need for supporting languages other than English. While more wide-spread or popular languages such as Spanish, French or Hindi are already integrated into existing home assistants like Google Home or Alexa, integration of other less-known languages such as Romanian is still missing. This paper explores the problem of Natural Language Understanding (NLU) applied to a Romanian home assistant. We propose a customized capsule neural network architecture that performs intent detection and slot filling in a joint manner and we evaluate how well it handles utterances containing various levels of complexity. The capsule network model shows a significant improvement in intent detection when compared to models built using the well-known Rasa NLU tool. Through error analysis, we observe clear error patterns that occur systematically. Variability in language when expressing one intent proves to be the biggest challenge encountered by the model. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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24 pages, 7676 KiB

Open AccessArticle

Reliable Link Level Routing Algorithm in Pipeline Monitoring Using Implicit Acknowledgements

by Carlos Egas Acosta., Felipe Gil-Castiñeira, Enrique Costa-Montenegro and Jorge Sá Silva

Sensors 2021, 21(3), 968; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030968 - 01 Feb 2021

Cited by 6 | Viewed by 2552

Abstract

End-to-end reliability for Wireless Sensor Network communications is usually provided by upper stack layers. Furthermore, most of the studies have been related to star, mesh, and tree topologies. However, they rarely consider the requirements of the multi-hop linear wireless sensor networks, with thousands of nodes, which are universally used for monitoring applications. Therefore, they are characterized by long delays and high energy consumption. In this paper, we propose an energy efficient link level routing algorithm that provides end-to-end reliability into multi-hop wireless sensor networks with a linear structure. The algorithm uses implicit acknowledgement to provide reliability and connectivity with energy efficiency, low latency, and fault tolerance in linear wireless sensor networks. The proposal is validated through tests with real hardware. The energy consumption and the delay are also mathematically modeled and analyzed. The test results show that our algorithm decreases the energy consumption and minimizes the delays when compared with other proposals that also apply the explicit knowledge technique and routing protocols with explicit confirmations, maintaining the same characteristics in terms of reliability and connectivity. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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21 pages, 4772 KiB

Open AccessFeature PaperArticle

Blockchain and Demand Response: Zero-Knowledge Proofs for Energy Transactions Privacy

by Claudia Daniela Pop, Marcel Antal, Tudor Cioara, Ionut Anghel and Ioan Salomie

Sensors 2020, 20(19), 5678; https://0-doi-org.brum.beds.ac.uk/10.3390/s20195678 - 05 Oct 2020

Cited by 37 | Viewed by 6092

Abstract

Nowadays, the adoption of demand response programs is still lagging due to the prosumers’ lack of awareness, fear of losing control and privacy of energy data, etc. Programs decentralization, by adopting promising technologies such as blockchain, may bring significant advantages in terms of transparency, openness, improved control, and increased active participation of prosumers. Nevertheless, even though in general the transparency of the public blockchain is a desirable feature in the energy domain, the prosumer energy data is sensitive and rather private, thus, a privacy-preserving solution is required. In this paper, we present a decentralized implementation of demand response programs on top of the public blockchain which deals with the privacy of the prosumer’s energy data using zero-knowledge proofs and validates on the blockchain the prosumer’s activity inside the program using smart contracts. Prosumer energy data is kept private, while on the blockchain it is stored a zero-knowledge proof that is generated by the prosumer itself allowing the implementation of functions to validate potential deviations from the request and settle prosumer’s activity. The solution evaluation results are promising in terms of ensuring the privacy of prosumer energy data stored in the public blockchain and detecting potential data inconsistencies. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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Review

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31 pages, 797 KiB

Open AccessReview

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

by Anton Terentev, Viktor Dolzhenko, Alexander Fedotov and Danila Eremenko

Sensors 2022, 22(3), 757; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030757 - 19 Jan 2022

Cited by 76 | Viewed by 11069

Abstract

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information. Full article

(This article belongs to the Special Issue Emerging Sensors Techniques and Technologies for Intelligent Environments)

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