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Internet of Things and Sensor Technologies in Smart Agriculture
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Internet of Things and Sensor Technologies in Smart Agriculture

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

Deadline for manuscript submissions: 25 September 2024 | Viewed by 8010

Special Issue Editors

Dr. Rossitza Goleva

E-Mail Website
Guest Editor
Department of Informatics, New Bulgarian University, 1618 g.k. Ovcha kupel 2, Sofia, Bulgaria
Interests: quality of service in communication networks; traffic engineering; cloud and fog computing; performance analyses
Dr. Radosveta Sokullu

E-Mail Website
Guest Editor
Deptartment of Electrcal and Electronic Engineering, Ege University, Bornova 35100, Turkey
Interests: Internet of Things; cellular radio; farming; learning (artificial intelligence); mobile computing; protocols; wireless LAN
Prof. Dr. Ciprian Dobre

E-Mail Website
Guest Editor
Computer Science Department, Politehnica University of Bucharest, 060042 Bucharest, Romania
Interests: mobile computing; pervasive systems; monitoring tools; context awareness
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nuno M. Garcia

E-Mail Website
Guest Editor
Computer Science Department, University of Beira Interior, 6200-001 Covilhã, Portugal
Interests: next-generation networks; algorithms for bio-signal processing; distributed and cooperative protocols; predictive algorithms for health and well-being
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue titled “Internet of Things and Sensor Technologies in Smart Agriculture”, which will be published in the journal Sensors (ISSN 1424-8220), aims to become a place for broad discussions on the recent scientific and technological advances in the field of smart edge computing, especially regarding its implementation in the agriculture, far-end food processing in fields and greenhouses, livestock production, biodiversity, and forestry management. Specific problems such as large areas needing to be covered; the lack of preciseness of the information; the lack of infrastructure for the mobile operators and local operations required for network deployment; the unreliability of the collected data; the definition of real-time and near-real-time services for end-users and stakeholders; problems with the distributed nature of the data collection; storage and processing methods implementing machine learning at the edge, fog, and cloud computing levels; privacy and data security; data sharing; data fusion; and data integration will be discussed.Reviews (including comprehensive reviews on complete sensor products), regular research papers, and short notes will be published in this Special Issue. We like to encourage scientists to publish their experimental and theoretical results in as much detail as possible. The full experimental details must be provided so that the results can be reproduced. Additionally, this Special Issue has three unique features:

  • Manuscripts regarding research proposals and research ideas are particularly welcome.
  • Electronic files and software providing full details of the calculation and experimental procedures can be deposited as supplementary material.
  • We will accept manuscripts regarding research projects financed via public funds to reach a broader audience.

The following topics cover the scope of this Special Issue:

  • Physical sensors;
  • Chemical sensors;
  • Biosensors;
  • Lab-on-a-chip technology;
  • Remote sensors;
  • Sensor networks;
  • Smart/Intelligent sensors;
  • Sensor devices;
  • Sensor technology and applications;
  • Sensing principles;
  • Optoelectronic and photonic sensors;
  • Optomechanical sensors;
  • Sensor arrays and chemometrics;
  • Micro and nanosensors;
  • Internet of Things;
  • Signal processing, data fusion, machine learning, and deep learning in sensor systems;
  • Sensor interface;
  • Human–computer interaction;
  • Advanced materials for sensing;
  • Sensing systems;
  • MEMS/NEMS;
  • Localization and object tracking;
  • Sensing and imaging;
  • Image sensors;
  • Vision/camera-based sensors;
  • Action recognition;
  • Machine/deep learning and artificial intelligence in sensing and imaging;
  • 3D sensing;
  • Communications and signal processing;
  • Wearable sensors, devices, and electronics;
  • Internet of Things to edge distributed computing;
  • Fog computing and data processing;
  • Cloud computing and data sharing.

Dr. Rossitza Goleva
Dr. Radosveta Sokullu
Prof. Dr. Ciprian Dobre
Prof. Dr. Nuno M. Garcia
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.

Published Papers (6 papers)

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Research

26 pages, 4982 KiB  
Article
Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience
by Radu Nicolae Pietraru, Adriana Olteanu, Ioana-Raluca Adochiei and Felix-Constantin Adochiei
Sensors 2024, 24(8), 2659; https://doi.org/10.3390/s24082659 - 22 Apr 2024
Viewed by 345
Abstract
This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. [...] Read more.
This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user’s decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution’s benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system. Full article
(This article belongs to the Special Issue Internet of Things and Sensor Technologies in Smart Agriculture)
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19 pages, 36547 KiB  
Article
Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Comparison between High-End and Low-Cost Multispectral Sensors
by Anna Teresa Seiche, Lucas Wittstruck and Thomas Jarmer
Sensors 2024, 24(5), 1544; https://doi.org/10.3390/s24051544 - 28 Feb 2024
Viewed by 717
Abstract
In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement [...] Read more.
In order to meet the increasing demand for crops under challenging climate conditions, efficient and sustainable cultivation strategies are becoming essential in agriculture. Targeted herbicide use reduces environmental pollution and effectively controls weeds as a major cause of yield reduction. The key requirement is a reliable weed detection system that is accessible to a wide range of end users. This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system. Pixel-based weed and crop classification was performed on UAV datasets collected with both sensors in maize using a U-Net. The training and testing data were generated via an index-based thresholding approach followed by annotation. As a result, the F1-score for the weed class reached 82% on the Altum system and 76% on the low-cost system, with recall values of 75% and 68%, respectively. Misclassifications occurred on the low-cost system images for small weeds and overlaps, with minor oversegmentation. However, with a precision of 90%, the results show great potential for application in automated weed control. The proposed system thereby enables sustainable precision farming for the general public. In future research, its spectral properties, as well as its use on different crops with real-time on-board processing, should be further investigated. Full article
(This article belongs to the Special Issue Internet of Things and Sensor Technologies in Smart Agriculture)
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20 pages, 15819 KiB  
Article
Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots
by Levent Türkler, Taner Akkan and Lütfiye Özlem Akkan
Sensors 2023, 23(22), 9244; https://doi.org/10.3390/s23229244 - 17 Nov 2023
Cited by 1 | Viewed by 1750
Abstract
In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. [...] Read more.
In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an electronic system can help achieve this objective. In this research, an experimental study was carried out to detect water leaks in the field in order to prevent water losses that can occur in agriculture, where water consumption is the highest. Therefore, in this study, low-cost embedded electronic hardware was developed to detect over-watering by means of normal and thermal camera sensors and to collect the required data, which can be installed on a mobile agricultural robot. For image processing and the diagnosis of abnormal conditions, the collected data were transferred to a personal computer server. Then, software was developed for both the low-cost embedded system and the personal computer to provide a faster detection and decision-making process. The physical and software system developed in this study was designed to provide a water leak detection process that has a minimum response time. For this purpose, mathematical and image processing algorithms were applied to obtain efficient water detection for the conversion of the thermal sensor data into an image, the image size enhancement using interpolation, the combination of normal and thermal images, and the calculation of the image area where water leakage occurs. The field experiments for this developed system were performed manually to observe the good functioning of the system. Full article
(This article belongs to the Special Issue Internet of Things and Sensor Technologies in Smart Agriculture)
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17 pages, 3709 KiB  
Article
Real-Time Person Detection in Wooded Areas Using Thermal Images from an Aerial Perspective
by Oscar Ramírez-Ayala, Iván González-Hernández, Sergio Salazar, Jonathan Flores and Rogelio Lozano
Sensors 2023, 23(22), 9216; https://doi.org/10.3390/s23229216 - 16 Nov 2023
Viewed by 816
Abstract
Detecting people in images and videos captured from an aerial platform in wooded areas for search and rescue operations is a current problem. Detection is difficult due to the relatively small dimensions of the person captured by the sensor in relation to the [...] Read more.
Detecting people in images and videos captured from an aerial platform in wooded areas for search and rescue operations is a current problem. Detection is difficult due to the relatively small dimensions of the person captured by the sensor in relation to the environment. The environment can generate occlusion, complicating the timely detection of people. There are currently numerous RGB image datasets available that are used for person detection tasks in urban and wooded areas and consider the general characteristics of a person, like size, shape, and height, without considering the occlusion of the object of interest. The present research work focuses on developing a thermal image dataset, which considers the occlusion situation to develop CNN convolutional deep learning models to perform detection tasks in real-time from an aerial perspective using altitude control in a quadcopter prototype. Extended models are proposed considering the occlusion of the person, in conjunction with a thermal sensor, which allows for highlighting the desired characteristics of the occluded person. Full article
(This article belongs to the Special Issue Internet of Things and Sensor Technologies in Smart Agriculture)
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27 pages, 5418 KiB  
Article
Structure, Functionality, Compatibility with Pesticides and Beneficial Microbes, and Potential Applications of a New Delivery System Based on Ink-Jet Technology
by Mohamed Idbella, Domenico Giusti, Gianluca Gulli and Giuliano Bonanomi
Sensors 2023, 23(6), 3053; https://doi.org/10.3390/s23063053 - 12 Mar 2023
Cited by 2 | Viewed by 1441
Abstract
Accurate application of agrochemicals is an important way to achieve efficient use of chemicals and to combine limited pollution with effective control of weeds, pests, and diseases. In this context, we investigate the potential application of a new delivery system based on ink-jet [...] Read more.
Accurate application of agrochemicals is an important way to achieve efficient use of chemicals and to combine limited pollution with effective control of weeds, pests, and diseases. In this context, we investigate the potential application of a new delivery system based on ink-jet technology. First, we describe the structure and functionality of ink-jet technology for agrochemical delivery. We then evaluate the compatibility of ink-jet technology with a range of pesticides (four herbicides, eight fungicides, and eight insecticides) and beneficial microbes, including fungi and bacteria. Finally, we investigated the feasibility of using ink-jet technology in a microgreens production system. The ink-jet technology was compatible with herbicides, fungicides, insecticides, and beneficial microbes that remained functional after passing through the system. In addition, ink-jet technology demonstrated higher area performance compared to standard nozzles under laboratory conditions. Finally, the application of ink-jet technology to microgreens, which are characterized by small plants, was successful and opened the possibility of full automation of the pesticide application system. The ink-jet system proved to be compatible with the main classes of agrochemicals and showed significant potential for application in protected cropping systems. Full article
(This article belongs to the Special Issue Internet of Things and Sensor Technologies in Smart Agriculture)
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13 pages, 2786 KiB  
Article
The Novel Combination of Nano Vector Network Analyzer and Machine Learning for Fruit Identification and Ripeness Grading
by Van Lic Tran, Thi Ngoc Canh Doan, Fabien Ferrero, Trinh Le Huy and Nhan Le-Thanh
Sensors 2023, 23(2), 952; https://doi.org/10.3390/s23020952 - 13 Jan 2023
Cited by 4 | Viewed by 2235
Abstract
Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared [...] Read more.
Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S11 and transmission coefficient S21. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network. Full article
(This article belongs to the Special Issue Internet of Things and Sensor Technologies in Smart Agriculture)
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