Special Issue "Digital Agriculture"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Dr. Alessandro Matese
Website
Guest Editor
Institute of BioEconomy, National Research Council (CNR-IBE), Florence, Italy
Interests: precision agriculture; remote sensing; biogeochemistry; meteorology; crop production
Special Issues and Collections in MDPI journals

Special Issue Information

“Precision Agriculture is a management strategy that gathers, processes, and analyses temporal, spatial, and individual data, and combines it with other information to support management decisions according to the estimated variability for the improved resource-use efficiency, productivity, quality, profitability, and sustainability of agricultural production”. The International Society of Precision Agriculture adopted this definition in 2019. Digital technologies are useful tools that can support farmers by improving efficiency, enabling better decisions, but they are not new to agriculture. Precision agriculture (PA) was born in the late 1980s with the use of global positioning system (GPS) guidance, yield mapping, and proximal and remote sensing systems for monitoring variations of soil and crop parameters within the field and linking them to variable rate technologies (VRT) to drive precise agronomic practices. The next step for PA is to exploit the potential of the data collected to provide adapted decisions. Large amounts of data generated by remote and proximal sensors must be able to aggregate and extract useful and intelligible information from stakeholders through the application of machine learning (ML) and artificial intelligence (IA) algorithms. This step is mandatory to link this data in a decision support systems (DSS) in order to understand field variability and promote practices for site-specific management. The ability to use technology to convert accurate data into usable knowledge to guide and support complex decision-making processes that will distinguish digital agriculture (DA) from PA, allow for the transition "from precision to decision". A key solution is the promotion of agricultural services that can produce clear and rapid decisions for the farmer using farm management software and ICT (information and communication technology) applications. The agricultural sector still faces a series of challenges before it can enter the DA era. These range from the cost of technological equipment, to the lack of broadband infrastructure in agricultural areas, to the intergenerational "electronic transition" and the collection and management of big data. Farmers will not invest in the technology without public funding; on the contrary, they will do so if they see the value deriving from the use of technology. Unfortunately, there is a lack of case studies that clearly describe the development of decision methodologies, and highlight the added value for the agricultural sector. The aim of this Special Issue is to promote the publication of case studies describing tools that digitally collect, store, analyze, and share electronic data and/or information along the agricultural value chain.

Dr. Alessandro Matese
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 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. Remote Sensing 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 2200 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

  • Agricultural decision support systems (AgriDSS)
  • Remote sensing from satellites or unmanned aerial vehicles
  • Proximal sensors
  • Blockchain-based platform integrated with remote sensing data and mobile solutions
  • Cloud computing/big data analysis tools
  • Artificial Intelligence (AI) and machine learning (ML) methodologies
  • Internet of Things (IoT)
  • Digital communications technologies, like mobile phones
  • Variable-rate input technologies
  • Automated machinery and agricultural robots

Published Papers (1 paper)

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Research

Open AccessArticle
Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds
Remote Sens. 2020, 12(14), 2331; https://doi.org/10.3390/rs12142331 - 20 Jul 2020
Abstract
Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of [...] Read more.
Canopy management operations, such as shoot thinning, leaf removal, and shoot trimming, are among the most relevant agricultural practices in viticulture. However, the supervision of these tasks demands a visual inspection of the whole vineyard, which is time-consuming and laborious. The application of photogrammetric techniques to images acquired with an Unmanned Aerial Vehicle (UAV) has proved to be an efficient way to measure woody crops canopy. Consequently, the objective of this work was to determine whether the use of UAV photogrammetry allows the detection of canopy management operations. A UAV equipped with an RGB digital camera was used to acquire images with high overlap over different canopy management experiments in four vineyards with the aim of characterizing vine dimensions before and after shoot thinning, leaf removal, and shoot trimming operations. The images were processed to generate photogrammetric point clouds of every vine that were analyzed using a fully automated object-based image analysis algorithm. Two approaches were tested in the analysis of the UAV derived data: (1) to determine whether the comparison of the vine dimensions before and after the treatments allowed the detection of the canopy management operations; and (2) to study the vine dimensions after the operations and assess the possibility of detecting these operations using only the data from the flight after them. The first approach successfully detected the canopy management. Regarding the second approach, significant differences in the vine dimensions after the treatments were detected in all the experiments, and the vines under the shoot trimming treatment could be easily and accurately detected based on a fixed threshold. Full article
(This article belongs to the Special Issue Digital Agriculture)
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