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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: closed (31 July 2021) | Viewed by 18907

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Dr. Alessandro Matese

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Institute of BioEconomy, National Research Council (CNR-IBE), Via Caproni 8, 50145 Firenze, Italy
Interests: environmental governance; institutional and ecological economics; climate change; biodiversity protection; land management
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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 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.

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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 (4 papers)

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Research

18 pages, 11229 KiB

Open AccessArticle

An Advanced Photogrammetric Solution to Measure Apples

by Eleonora Grilli, Roberto Battisti and Fabio Remondino

Remote Sens. 2021, 13(19), 3960; https://doi.org/10.3390/rs13193960 - 02 Oct 2021

Cited by 10 | Viewed by 2618

Abstract

This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers’ and agronomists’ fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account. Full article

(This article belongs to the Special Issue Digital Agriculture)

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27 pages, 17653 KiB

Open AccessArticle

A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the ‘Cipolla Rossa di Tropea’ (Italy)

by Gaetano Messina, Jose M. Peña, Marco Vizzari and Giuseppe Modica

Remote Sens. 2020, 12(20), 3424; https://doi.org/10.3390/rs12203424 - 18 Oct 2020

Cited by 51 | Viewed by 6619

Abstract

Precision agriculture (PA) is a management strategy that analyzes the spatial and temporal variability of agricultural fields using information and communication technologies with the aim to optimize profitability, sustainability, and protection of agro-ecological services. In the context of PA, this research evaluated the reliability of multispectral (MS) imagery collected at different spatial resolutions by an unmanned aerial vehicle (UAV) and PlanetScope and Sentinel-2 satellite platforms in monitoring onion crops over three different dates. The soil adjusted vegetation index (SAVI) was used for monitoring the vigor of the study field. Next, the vigor maps from the two satellite platforms with those derived from UAV were compared by statistical analysis in order to evaluate the contribution made by each platform for monitoring onion crops. Besides, the two coverage’s classes of the field, bare soil and onions, were spatially identified using geographical object-based image classification (GEOBIA), and their spectral contribution was analyzed comparing the SAVI calculated considering only crop pixels (i.e., SAVI onions) and that calculated considering only bare soil pixels (i.e., SAVI soil) with the SAVI from the three platforms. The results showed that satellite imagery, coherent and correlated with UAV images, could be useful to assess the general conditions of the field while UAV permits to discriminate localized circumscribed areas that the lowest resolution of satellites missed, where there are conditions of inhomogeneity in the field, determined by abiotic or biotic stresses. Full article

(This article belongs to the Special Issue Digital Agriculture)

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16 pages, 5111 KiB

Open AccessArticle

Spatial and Temporal Pasture Biomass Estimation Integrating Electronic Plate Meter, Planet CubeSats and Sentinel-2 Satellite Data

by Juan Gargiulo, Cameron Clark, Nicolas Lyons, Gaspard de Veyrac, Peter Beale and Sergio Garcia

Remote Sens. 2020, 12(19), 3222; https://doi.org/10.3390/rs12193222 - 03 Oct 2020

Cited by 18 | Viewed by 4071

Abstract

There is a substantial opportunity to lift feed utilization and profitability on pasture-based dairy systems through both increased pasture monitoring accuracy and frequency. The first objective of this experiment was to determine the impact of the number of electronic rising plate meter (RPM) readings and walking pattern on the accuracy of the RPM to determine pasture biomass. The second objective was to evaluate current satellite technology (i.e., small CubeSats and traditional large satellites) in combination with the electronic RPM as an accurate tool for systematic pasture monitoring. The experiment was conducted from October to December 2019 at Camden, Australia. Two experimental paddocks, each of 1.1 ha, were sown with annual ryegrass and monitored with an electronic RPM integrated with Global Navigation Satellite System and with two different satellites (Planet CubeSats and Sentinel-2 satellite). Here we show that 70 RPM readings achieve a ± 5% error in the pasture biomass estimations (kg DM/ha), with no effect of the walking pattern on accuracy. The normalized difference vegetation index (NDVI) derived from satellites showed a good correlation with pasture biomass estimated using the electronic RPM (R² 0.74–0.94). Satellite pasture biomass and growth rate estimations were similar to RPM in one regrowth period but underestimated by ≈20% in the other. Our results also reveal that the accuracy of uncalibrated satellites (i.e., biomass estimated using NDVI to kg DM/ha standard equations) is low (R² 0.61, RMSE 566–1307 kg DM/ha). However, satellites calibrated with a RPM showed greater accuracy in the estimations (R² 0.72, RMSE 255 kg DM/ha). Current satellite technology, when used with the electronic RPM, has the potential to not only reduce the time required to monitor pasture biomass manually but provide finer scale measurements of pasture biomass within paddocks. Further work is required to test this hypothesis, both spatially and temporally. Full article

(This article belongs to the Special Issue Digital Agriculture)

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22 pages, 7144 KiB

Open AccessArticle

Monitoring Vineyard Canopy Management Operations Using UAV-Acquired Photogrammetric Point Clouds

by Francisca López-Granados, Jorge Torres-Sánchez, Francisco M. Jiménez-Brenes, Oihane Oneka, Diana Marín, Maite Loidi, Ana I. de Castro and L. G. Santesteban

Remote Sens. 2020, 12(14), 2331; https://doi.org/10.3390/rs12142331 - 20 Jul 2020

Cited by 14 | Viewed by 4545

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 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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