Special Issue "Emerging Sensor Technology in Agriculture"
Deadline for manuscript submissions: 31 December 2019.
Dr. Sigfredo Fuentes
Associate Professor in University of Melbourne, School of Agriculture and Food Sciences; Faculty of Veterinary and Agricultural Sciences, Parkville, Australia
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Interests: digital agriculture; food and wine sciences; plant physiology; remote sensing; climate change; robotics applied to agriculture and computer programming
Challenges imposed by climate change have caused significant interest and investments, from different countries, into research areas related to smart digital agriculture. This has been notably triggered by an impending population increase to 9.2 billion in 2050, and the requirement of producing 70% more food by 2050 in half of arable land available today (FAO).
In order to be successful in overcoming the effects of climate change, and to remain competitive and sustainable as a country in the agricultural sector, there is a need to acknowledge these challenges and support research and applications in the development of new and emerging sensor technologies and their applications in agriculture. The development of new and emerging technologies applied to sensor networks will help to overcome these issues by basing decision making on more accurate, meaningful data with high spatial and temporal resolutions.
Sensor technology and sensor networks using telemetry systems and the Internet of Things (IoT) are becoming important for research areas that can be applied to digital agriculture. The key challenge in the production of accurate agricultural models relies critically on timely provision of high-quality, geospatially-distributed data. This requires the development of complex workflows of real-time sensor calibration, data transfer, image processing and interpretation, as well as integration in optimal and high-performing computational nodes and networks. An example is imaging sensor data, where image sensors need to be radiometrically and geometrically calibrated so that each pixel value can be reliably converted to an at-surface reflectance value. Conventional sensing systems deploy time-consuming post-processing, which depends on specialized skills and specific software, which significantly delays the delivery of the final information to users. The aim of this particular call is focused on systems that provide automated integrated set of tools that can standardize the key components of aerial and ground sensor data processing to empowering industry and academics to focus on innovation. The proposed system will enable near-real-time distribution of monitored aspects of soil–plants and atmospheric factors that allows data mapping and delivery via mobile devices.
The technology proposed can include also a cloud computing framework for sensor calibration, processing, fusion, and classification to reduce the complexity and time required to develop workflows.
Papers submitted based on the following aspects will be highly considered:
- Research based on the framework to process and combine ground based sensor networks, meteorological information and remotely sensed data from proximal and UAVs based technology to rapidly produce high quality geospatial products that help visualize our environment in extreme detail. Satellite based research will be excluded from this call.
- Modelling papers using sensor or remote sensing technology coupled with machine learning modelling that targets important factors in the agricultural decision making process, such as irrigation scheduling, canopy and fertilizer management, pest and disease management, among others.
- Research that has used cloud computing on High-Performance Computing (HPC) platforms that enables rapid and automated processing of aerial imagery and ground-based sensor network data, streamlining the process, from data acquisition to data analysis.
- Papers showing the shared knowledge and experience gained through collaboration between industry and academics with centralized development efforts for sensor data processing and visualization algorithms, leading to a higher quality of geospatial products, will be also considered.
Potential topics include, but are not limited to, the following:
- New sensor development and applications for agriculture and forestry trials.
- Sensor network development, data transmission, self-healing and redundancy considerations.
- Machine learning modelling for geospatial information targeting agricultural decision making criteria such as plant water status, canopy growth, nutritional level, early pest and disease management, among others.
- Remote sensing using unmanned aerial vehicles (UAV) integrated with sensor network technology.
- Visualization systems and software platforms developed to integrate sensor networks for decision-making processes.
- Low-cost smart sensors applicable to agriculture.
- Development of integrated models with sensor networks and applications in agriculture and forestry environments.
Dr. Sigfredo Fuentes
Dr. Carlos Poblete-Echeverria
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 papers will be 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 1800 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.
- Sensor Networks
- Internet of Things
- Machine Learning
- Unmanned Aerial Vehicles
- Remote Sensing