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Drones for Precision Agriculture: Remote Sensing Applications

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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 (30 September 2021) | Viewed by 24723

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Special Issue Editors

Prof. Dr. Javier J Cancela

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Guest Editor

GI-1716, Projects and Planification, Dpto. Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escola Politécnica Superior de Enxeñaría, Rúa Benigno Ledo s/n, 27002 Lugo, Spain
Interests: crop water requirements; soil–water management; irrigation management; soil science; fertility; precision viticulture; remote sensing; unmanned aerial vehicles; satellite imagery
Special Issues, Collections and Topics in MDPI journals

Dr. Rocío Ballesteros González

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Guest Editor

Crop Production and Agricultural Technology Department, Higher Agricultural and Forestry Engineering School, University of Castilla-La Mancha, Campus Universitario s/n, 02051 Albacete, Spain
Interests: precision agriculture; forestry; unmanned aerial vehicles; satellite imagery; irrigation management; soil science; fertility; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is very well known that based on estimations on future growth, the world population will reach 9100 million by 2050. Therefore, food security should be considered as one of the main targets following the 2030 Sustainable Development Goals. Further, it is not possible to speak about global population increase without considering global warming future scenarios. Water scarcity and droughts will increase overall in those arid areas where water availability is already a problem. Precision agriculture should be considered a useful tool to face all these global challenges. Among those, unmanned aerial vehicles, known as drones, have evolved rapidly in the last decade. Currently, more affordable and easier-to-use drones have found wide use in improving sustainable crop production.

Different sensors, e.g., RGB, multi- and hyperspectral cameras or LIDAR technology, mounted onboard improve sustainable crop production for multiple aspects/applications such as:

3-D maps for soil analysis;
Mid-season crop health monitoring;
Irrigation equipment monitoring;
Pesticide spraying;
Increase the yield and overall quality;
Wildlife detection.

The continuous development of sensors and drones has generated an increase in the information obtained, requiring the use of more complex data analysis techniques, integrating all available data. Image processing and pattern recognition are required to implement a final application successfully, e.g., using a vegetation index for crop monitoring. Different data analysis techniques for applications with remote sensors need a standardization to advance in the consolidation of drones in precision agriculture.

For all these reasons, basic research studies applied to precision agriculture, in any of the subjects presented, or as a whole, are well received—especially those studies that address data analysis techniques, ‘exportable’ to other applications and crops, facing an ‘open science’. Successful experiences of application of the use of drones in agriculture (woody crops, horticulture, cereals, rice, etc.) based on results of a long-time scale are also expected.

Dr. Javier J. Cancela
Dr. Rocío Ballesteros González
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. 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 2700 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

data processing
data analysis techniques
soil analysis
segmentation
monitoring
variable rate
yield and overall quality

Published Papers (5 papers)

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Research

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21 pages, 3635 KiB

Open AccessArticle

Estimation of Paddy Rice Nitrogen Content and Accumulation Both at Leaf and Plant Levels from UAV Hyperspectral Imagery

by Li Wang, Shuisen Chen, Dan Li, Chongyang Wang, Hao Jiang, Qiong Zheng and Zhiping Peng

Remote Sens. 2021, 13(15), 2956; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152956 - 27 Jul 2021

Cited by 44 | Viewed by 4223

Abstract

Remote sensing-based mapping of crop nitrogen (N) status is beneficial for precision N management over large geographic regions. Both leaf/canopy level nitrogen content and accumulation are valuable for crop nutrient diagnosis. However, previous studies mainly focused on leaf nitrogen content (LNC) estimation. The effects of growth stages on the modeling accuracy have not been widely discussed. This study aimed to estimate different paddy rice N traits—LNC, plant nitrogen content (PNC), leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA)—from unmanned aerial vehicle (UAV)-based hyperspectral images. Additionally, the effects of the growth stage were evaluated. Univariate regression models on vegetation indices (VIs), the traditional multivariate calibration method, partial least squares regression (PLSR) and modern machine learning (ML) methods, including artificial neural network (ANN), random forest (RF), and support vector machine (SVM), were evaluated both over the whole growing season and in each single growth stage (including the tillering, jointing, booting and heading growth stages). The results indicate that the correlation between the four nitrogen traits and the other three biochemical traits—leaf chlorophyll content, canopy chlorophyll content and aboveground biomass—are affected by the growth stage. Within a single growth stage, the performance of selected VIs is relatively constant. For the full-growth-stage models, the performance of the VI-based models is more diverse. For the full-growth-stage models, the transformed chlorophyll absorption in the reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI) performs best for LNC, PNC and PNA estimation, while the three band vegetation index (TBVI_Tian) performs best for LNA estimation. There are no obvious patterns regarding which method performs the best of the PLSR, ANN, RF and SVM in either the growth-stage-specific or full-growth-stage models. For the growth-stage-specific models, a lower mean relative error (MRE) and higher R² can be acquired at the tillering and jointing growth stages. The PLSR and ML methods yield obviously better estimation accuracy for the full-growth-stage models than the VI-based models. For the growth-stage-specific models, the performance of VI-based models seems optimal and cannot be obviously surpassed. These results suggest that building linear regression models on VIs for paddy rice nitrogen traits estimation is still a reasonable choice when only a single growth stage is involved. However, when multiple growth stages are involved or missing the phenology information, using PLSR or ML methods is a better option. Full article

(This article belongs to the Special Issue Drones for Precision Agriculture: Remote Sensing Applications)

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16 pages, 5800 KiB

Open AccessArticle

A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery

by Rodrigo Nogueira Martins, Francisco de Assis de Carvalho Pinto, Daniel Marçal de Queiroz, Domingos Sárvio Magalhães Valente and Jorge Tadeu Fim Rosas

Remote Sens. 2021, 13(2), 263; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020263 - 13 Jan 2021

Cited by 20 | Viewed by 4333

Abstract

Coffee ripeness monitoring is a key indicator for defining the moment of starting the harvest, especially because the coffee quality is related to the fruit ripeness degree. The most used method to define the start of harvesting is by visual inspection, which is time-consuming, labor-intensive, and does not provide information on the entire area. There is a lack of new techniques or alternative methodologies to provide faster measurements that can support harvest planning. Based on that, this study aimed at developing a vegetation index (VI) for coffee ripeness monitoring using aerial imagery. For this, an experiment was set up in five arabica coffee fields in Minas Gerais State, Brazil. During the coffee ripeness stage, four flights were carried out to acquire spectral information on the crop canopy using two quadcopters, one equipped with a five-band multispectral camera and another with an RGB (Red, Green, Blue) camera. Prior to the flights, manual counts of the percentage of unripe fruits were carried out using irregular sampling grids on each day for validation purposes. After image acquisition, the coffee ripeness index (CRI) and other five VIs were obtained. The CRI was developed combining reflectance from the red band and from a ground-based red target placed on the study area. The effectiveness of the CRI was compared under different analyses with traditional VIs. The CRI showed a higher sensitivity to discriminate coffee plants ready for harvest from not-ready for harvest in all coffee fields. Furthermore, the highest R² and lowest RMSE values for estimating the coffee ripeness were also presented by the CRI (R²: 0.70; 12.42%), whereas the other VIs showed R² and RMSE values ranging from 0.22 to 0.67 and from 13.28 to 16.50, respectively. Finally, the study demonstrated that the time-consuming fieldwork can be replaced by the methodology based on VIs. Full article

(This article belongs to the Special Issue Drones for Precision Agriculture: Remote Sensing Applications)

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Graphical abstract

16 pages, 36677 KiB

Open AccessArticle

Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

by Haibin Gu, Zhe Lin, Wenxuan Guo and Sanjit Deb

Remote Sens. 2021, 13(1), 145; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010145 - 04 Jan 2021

Cited by 11 | Viewed by 4172

Abstract

Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (T_s) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R² (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale. Full article

(This article belongs to the Special Issue Drones for Precision Agriculture: Remote Sensing Applications)

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18 pages, 2913 KiB

Open AccessArticle

Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees

by Víctor Blanco, Pedro José Blaya-Ros, Cristina Castillo, Fulgencio Soto-Vallés, Roque Torres-Sánchez and Rafael Domingo

Remote Sens. 2020, 12(15), 2359; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152359 - 23 Jul 2020

Cited by 19 | Viewed by 3756

Abstract

The present work aims to assess the usefulness of five vegetation indices (VI) derived from multispectral UAS imagery to capture the effects of deficit irrigation on the canopy structure of sweet cherry trees (Prunus avium L.) in southeastern Spain. Three irrigation treatments were assayed, a control treatment and two regulated deficit irrigation treatments. Four airborne flights were carried out during two consecutive seasons; to compare the results of the remote sensing VI, the conventional and continuous water status indicators commonly used to manage sweet cherry tree irrigation were measured, including midday stem water potential (Ψ_s) and maximum daily shrinkage (MDS). Simple regression between individual VIs and Ψ_s or MDS found stronger relationships in postharvest than in preharvest. Thus, the normalized difference vegetation index (NDVI), resulted in the strongest relationship with Ψ_s (r² = 0.67) and MDS (r² = 0.45), followed by the normalized difference red edge (NDRE). The sensitivity analysis identified the optimal soil adjusted vegetation index (OSAVI) as the VI with the highest coefficient of variation in postharvest and the difference vegetation index (DVI) in preharvest. A new index is proposed, the transformed red range vegetation index (TRRVI), which was the only VI able to statistically identify a slight water deficit applied in preharvest. The combination of the VIs studied was used in two machine learning models, decision tree and artificial neural networks, to estimate the extra labor needed for harvesting and the sweet cherry yield. Full article

(This article belongs to the Special Issue Drones for Precision Agriculture: Remote Sensing Applications)

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Review

Jump to: Research

30 pages, 1637 KiB

Open AccessReview

A Bibliometric Review of the Use of Unmanned Aerial Vehicles in Precision Agriculture and Precision Viticulture for Sensing Applications

by Abhaya Pal Singh, Amol Yerudkar, Valerio Mariani, Luigi Iannelli and Luigi Glielmo

Remote Sens. 2022, 14(7), 1604; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071604 - 27 Mar 2022

Cited by 31 | Viewed by 4806

Abstract

This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus database. The analysis shows that the researchers from the United States, China, Italy and Spain lead the precision agriculture through UAV applications. In terms of employing UAVs in PV, researchers from Italy are fast extending their work followed by Spain and finally the United States. Additionally, the paper provides a comprehensive study on popular journals for academicians to submit their work, accessible funding organizations, popular nations, institutions, and authors conducting research on utilizing UAVs for precision agriculture. Finally, this study emphasizes the necessity of using UAVs in PV as well as future possibilities. Full article

(This article belongs to the Special Issue Drones for Precision Agriculture: Remote Sensing Applications)

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