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Internet-of-Things for Precision Agriculture (IoAT) 2021-2022

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

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 5797

Special Issue Editor

Special Issue Information

Dear Colleagues,

Precision agriculture is important for to satisfying the ever-increasing worldwide demand for food products of good quality, whilst allaying the societal concerns over animal welfare and heavily reducing the strain on environmental resources. The principle is that if the needs of animals and crops are satisfied at the highest granularity, then farmers and the supply chain, including consumers, will benefit. Recently, the sector has been subject to an increasing drive towards efficiency and performance enhancement to improve sustainability against a backdrop of increasing demand and volatile trading environments that may emerge as a consequence of political change.

A direct consequence is that farmers have less time to execute traditional practices and are becoming increasingly reliant on technology. Thus, there is a growing range of opportunities for the delivery of precision farming solutions through the integration of a mix of hardware and software technologies. In turn, the turn to new business models based on provisioning a range of services to the agricultural community becomes possible, further fueling the ready uptake of technology for the benefit of all operating within the supply chain.

The solutions required to support this evolution harness a number of technologies that follow Internet of Things (IoT) principles. IoT is a platform that allows a network of devices to communicate, gather data and process information collaboratively in the service of individuals or processes. The solutions rely on engineering data driven by consideration of the impacts in order for the derived product/application/service to be not only fit-for-purpose, but they can also be easily deployed and maintained with a minimum of agitation to a sector that has followed entrenched practices for many years. These platforms generate large amounts of data in a variety of formats and is, thus, ‘Big’ in that it comprising many different streams of single stranded data, but is markedly different from the ‘Big Science Data’ sets routinely encountered in drug development and oil exploration. The data can then be translated into actionable information through machine learning, artificial intelligence, statistical and other advanced techniques, models and methods, to create value for the spectrum of stakeholders across the agricultural supply chain; therefore, optimizing production and sustaining the security of the food supply.

This Special Issue will capture the latest innovations from fundamental scientific concepts to commercially robust IoT-inspired solutions (Internet of Agricultural Things—IoAT) relevant to the development and adoption of precision agriculture methodologies. The Guest Editors invite submissions that range from new sensors through cloud-based computing to data-driven applications/services. Topics of interest include, but are not limited to, the following themes:

  • Intelligent Sensing Technologies
  • Data Architectures and Management
  • Edge Computing
  • Network and Communications Technologies
  • IoT Platform Integration
  • Machine Learning and Artificial Intelligence
  • Emerging Applications/Services and Cloud Analytics
  • Information Visualization
  • Security, Privacy and Trust
  • Inter-Operability and Standards
  • Emerging Business Models

Prof. Dr. Ivan Andonovic
Guest Editor

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 (2 papers)

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Research

22 pages, 7062 KiB  
Article
Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks
by Lizy Abraham, Steven Davy, Muhammad Zawish, Rahul Mhapsekar, John A. Finn and Patrick Moran
Sensors 2022, 22(6), 2190; https://doi.org/10.3390/s22062190 - 11 Mar 2022
Cited by 1 | Viewed by 2241
Abstract
Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore there is a strong need for digital methods to improve [...] Read more.
Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore there is a strong need for digital methods to improve the speed and efficiency of identification and differentiation of farmland habitats. This is challenging because of the large number of subcategories having nearly indistinguishable features within the habitat classes. Heterogeneity among sites within the same habitat class is another problem. Therefore, this research work presents a preliminary technique for accurate farmland classification using stacked ensemble deep convolutional neural networks (DNNs). The proposed approach has been validated on a high-resolution dataset collected using drones. The image samples were manually labelled by the experts in the area before providing them to the DNNs for training purposes. Three pre-trained DNNs customized using the transfer learning approach are used as the base learners. The predicted features derived from the base learners were then used to train a DNN based meta-learner to achieve high classification rates. We analyse the obtained results in terms of convergence rate, confusion matrices, and ROC curves. This is a preliminary work and further research is needed to establish a standard technique. Full article
(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT) 2021-2022)
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19 pages, 6026 KiB  
Article
Joint Communication and Sensing: A Proof of Concept and Datasets for Greenhouse Monitoring Using LoRaWAN
by Ritesh Kumar Singh, Mohammad Hasan Rahmani, Maarten Weyn and Rafael Berkvens
Sensors 2022, 22(4), 1326; https://doi.org/10.3390/s22041326 - 09 Feb 2022
Cited by 6 | Viewed by 2874
Abstract
In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial intelligence (AI) have created opportunities to assist farmers further in detecting disease [...] Read more.
In recent years, greenhouse-based precision agriculture (PA) has been strengthened by utilization of Internet of Things applications and low-power wide area network communication. The advancements in multidisciplinary technologies such as artificial intelligence (AI) have created opportunities to assist farmers further in detecting disease and poor nutrition of plants. Neural networks and other AI techniques need an initial set of measurement campaigns along with extensive datasets as a training set to baseline and evolve different applications. This paper presents LoRaWAN-based greenhouse monitoring datasets over a period of nine months. The dataset has both the network and sensing information from multiple sensor nodes for tomato crops in two different greenhouse environments. The goal is to provide the research community with a dataset to evaluate performance of LoRaWAN inside a greenhouse and develop more efficient PA monitoring techniques. In this paper, we carried out an exploratory data analysis to infer crop growth by analyzing just the LoRaWAN signals and without inclusion of any extra hardware. This work uses a multilayer perceptron artificial neural network to predict the weekly plant growth, trained using RSSI value from sensor data and manual measurement of plant height from the greenhouse. We developed this proof of concept of joint communication and sensing by using generated dataset from the “Proefcentrum Hoogstraten” greenhouse in Belgium. Results for the proposed method yield a root mean square error of 10% in detecting the average plant height inside a greenhouse. In future, we can use this concept of landscape sensing for different supplementary use-cases and to develop optimized methods. Full article
(This article belongs to the Special Issue Internet-of-Things for Precision Agriculture (IoAT) 2021-2022)
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