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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 52339

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Special Issue Editor

Dr. Vijay Singh

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Guest Editor

Virginia Polytechnic Institute and State University, Eastern Shore Agricultural Research and Extension Center 33446 Research Dr., Painter, VA 23420, USA
Interests: watershed modeling; erosion and sediment transport in upland watersheds; streamflow forecasting; dam break analysis; entropy-based modeling; network design; groundwater modeling; hydrologic impacts of climate change

Special Issue Information

Dear Colleagues,

Changing climatic conditions and increased environmental pressure on limited agricultural resources to feed a population of 10 billion by 2050 have posed a great challenge for sustainable crop production. This has led to the revolution and adoption of precision-based technologies which can assist in ecological enhancement and resource conservation. The global precision agriculture market is valued at USD 4.7 billion (2019) and is expected to grow at a rate of 13% from 2020 to 2027 (GVR 2020). Remote sensing is an important component of precision agriculture and has shown tremendous improvements over the past decade in terms of data collection, accuracy, systems, and methodologies for high-resolution imagery.

Remote sensing can provide a convenient solution to ground-based manual scouting for crop monitoring, disease inspection, insect prediction, weed classification, water management, yield estimations, and land use. It can provide precise and timely data collection, which may help in implementing short- and long-term strategies for crop management. In recent years, the agricultural sector has witnessed an increased use of advanced technologies such as satellites, drones, robots, and other sensor guided systems, such as handheld devices for proximal sensing. While the potential of satellite-based remote sensing has been explored in the past five decades, the higher resolution imagery and cloud cover avoidance for accurately mapping agricultural fields with flying machines have yet to be fully explored. The use of Unmanned Aerial Systems (UAS) and robots for more frequently capturing on-demand and accurate data has led to their applications for assisting in crop management decisions and varietal performance evaluations in breeding programs. Along with hardware, software technologies that assist in data analysis and make use of artificial intelligence and machine learning have shown the potential for near real-time data analytics for farm operations.

The use of satellite-based remote sensing, robotics, drones, farm automation, etc. are expected to gain momentum in coming days as these technologies are the keys for smart agriculture. We are at the brink of a paradigm shift in agricultural sector and will see the inclusion of advanced technologies for climate-smart production systems and precision agriculture at a higher rate of adoption. Hence, it is important to learn and gather information on related research in different parts of the world and collectively utilize that knowledge and outcome for the improvement of agricultural practices.

Therefore, we would like to invite review, research, and methodology articles, and opinions on remote sensing in agriculture. The Special Issue would include study areas such as sensor-based technologies for soil-, weed-, insect-, disease-mapping, phenotyping, varietal evaluations and genetic improvements, water-management, and other sensor-based applications in crop and range lands. Articles on the use of LiDAR, vegetative indices, RGB-, hyperspectral-, multi-spectral- and thermal-imagery are highly encouraged. Methods and approaches that utilize artificial intelligence programming and neural networks for real-time decision making for farm operations would also be considered.

Dr. Vijay Singh
Guest Editor

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Keywords

Drones
hyperspectral
LiDAR
multispectral
precision agriculture
robotics
satellite
sensor
UAS
vegetation indices

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Published Papers (13 papers)

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17 pages, 5037 KiB

Open AccessArticle

Relationships between Soil Electrical Conductivity and Sentinel-2-Derived NDVI with pH and Content of Selected Nutrients

by Piotr Mazur, Dariusz Gozdowski and Agnieszka Wnuk

Agronomy 2022, 12(2), 354; https://doi.org/10.3390/agronomy12020354 - 31 Jan 2022

Cited by 10 | Viewed by 4262

Abstract

Site-specific crop management demands maps which present the content of the main macronutrients. Such maps are prepared based on optimized soil sampling within management zones, which should be quite homogenous according to nutrient content, especially the content of potassium and phosphorus. Delineation of management zones is very often conducted using soil apparent electrical conductivity (EC) or other variables related to soil condition, including satellite-derived vegetation indices. In this study conducted in North-Western Poland, relationships between soil electrical conductivity and the satellite-derived normalized difference vegetation index (NDVI) of various crops (wheat, barley, and rapeseed) with soil pH and content of P, K, and Mg were evaluated. Strong relationships were observed between NDVI of cereals with potassium content in soil. Correlation coefficients for wheat ranged from 0.37 to 0.60 for average potassium content for three years and from 0.05 to 0.63 for barley. Stronger relationships were observed for the years 2018 and 2019 when NDVI was based on Sentinel-2 data, while weaker for year 2017 when Landsat 8 NDVI was used. Relationships between EC and macronutrients content were similar to those observed with NDVI. Satellite-derived NDVI of cereals can be used as a variable for the delineation of within-field management zones. The same relationships were much weaker and not consistent for winter rapeseed. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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14 pages, 3715 KiB

Open AccessArticle

A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

by Ali Mirzazadeh, Afshin Azizi, Yousef Abbaspour-Gilandeh, José Luis Hernández-Hernández, Mario Hernández-Hernández and Iván Gallardo-Bernal

Agronomy 2021, 11(11), 2364; https://doi.org/10.3390/agronomy11112364 - 22 Nov 2021

Cited by 6 | Viewed by 2248

Abstract

Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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20 pages, 3881 KiB

Open AccessEditor’s ChoiceArticle

Economic Comparison of Satellite, Plane and UAV-Acquired NDVI Images for Site-Specific Nitrogen Application: Observations from Italy

by Marco Sozzi, Ahmed Kayad, Stefano Gobbo, Alessia Cogato, Luigi Sartori and Francesco Marinello

Agronomy 2021, 11(11), 2098; https://doi.org/10.3390/agronomy11112098 - 20 Oct 2021

Cited by 31 | Viewed by 4586

Abstract

Defining the most profitable remote sensing platforms is a difficult decision-making process, as it requires agronomic and economic considerations. In this paper, the price and profitability of three levels of remote sensing platforms were evaluated to define a decision-making process. Prices of satellite, plane and UAV-acquired vegetation indices were collected in Italy during 2020 and compared to the economic benefits resulting from variable rate nitrogen application, according to a bibliographic meta-analysis performed on grains. The quality comparison of these three technologies was performed considering the error propagation in the NDVI formula. The errors of the single bands were used to assess the optical properties of the sensors. Results showed that medium-resolution satellite data with good optical properties could be profitably used for variable rate nitrogen applications starting from 2.5 hectares, in case of medium resolution with good optical properties. High-resolution satellites with lower optical quality were profitable starting from 13.2 hectares, while very high-resolution satellites with good optical properties could be profitably used starting from 76.8 hectares. Plane-acquired images, which have good optical properties, were profitable starting from 66.4 hectares. Additionally, a reference model for satellite image price is proposed. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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16 pages, 611 KiB

Open AccessArticle

Remote Sensing for Palmer Amaranth (Amaranthus palmeri S. Wats.) Detection in Soybean (Glycine max (L.) Merr.)

by John T. Sanders, Eric A. L. Jones, Robert Austin, Gary T. Roberson, Robert J. Richardson and Wesley J. Everman

Agronomy 2021, 11(10), 1909; https://doi.org/10.3390/agronomy11101909 - 23 Sep 2021

Cited by 5 | Viewed by 2066

Abstract

Field studies were conducted in 2016 and 2017 to determine if multispectral imagery collected from an unmanned aerial vehicle (UAV) equipped with a five-band sensor could successfully identify Palmer amaranth (Amaranthus palmeri) infestations of various densities growing among soybeans (Glycine max [L.] Merr.). The multispectral sensor captures imagery from five wavebands: 475 (blue), 560 (green), 668 (red), 840 (near infrared [NIR]), and 717 nm (red-edge). Image analysis was performed to examine the spectral properties of discrete Palmer amaranth and soybean plants at various weed densities using these wavebands. Additionally, imagery was subjected to supervised classification to evaluate the usefulness of classification as a tool to differentiate the two species in a field setting. Date was a significant factor influencing the spectral reflectance values of the Palmer amaranth densities. The effects of altitude on reflectance were less clear and were dependent on band and density being evaluated. The near infrared (NIR) waveband offered the best resolution in separating Palmer amaranth densities. Spectral separability in the other wavebands was less defined, although low weed densities were consistently able to be discriminated from high densities. Palmer amaranth and soybean were found to be spectrally distinct regardless of imaging date, weed density, or waveband. Soybean exhibited overall lower reflectance intensity than Palmer amaranth across all wavebands. The reflectance of both species within blue, green, red, and red-edge wavebands declined as the season progressed, while reflectance in NIR increased. Near infrared and red-edge wavebands were shown to be the most useful for species discrimination and maintained their utility at most weed densities. Palmer amaranth weed densities were found to be spectrally distinct from one another in all wavebands, with greatest distinction when using the red, NIR and red-edge wavebands. Supervised classification in a two-class system was consistently able to discriminate between Palmer amaranth and soybean with at least 80% overall accuracy. The incorporation of a weed density component into these classifications introduced an error of 65% or greater into these classifications. Reducing the number of classes in a supervised classification system could improve the accuracy of discriminating between Palmer amaranth and soybean. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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21 pages, 5379 KiB

Open AccessArticle

Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning

by Dorijan Radočaj, Mladen Jurišić, Mateo Gašparović, Ivan Plaščak and Oleg Antonić

Agronomy 2021, 11(8), 1620; https://doi.org/10.3390/agronomy11081620 - 16 Aug 2021

Cited by 19 | Viewed by 3528

Abstract

The determination of cropland suitability is a major step for adapting to the increased food demands caused by population growth, climate change and environmental contamination. This study presents a novel cropland suitability assessment approach based on machine learning, which overcomes the limitations of the conventional GIS-based multicriteria analysis by increasing computational efficiency, accuracy and objectivity of the prediction. The suitability assessment method was developed and evaluated for soybean cultivation within two 50 × 50 km subsets located in the continental biogeoregion of Croatia, in the four-year period during 2017–2020. Two biophysical vegetation properties, leaf area index (LAI) and a fraction of absorbed photosynthetically active radiation (FAPAR), were utilized to train and test machine learning models. The data derived from a medium-resolution satellite mission PROBA-V were prime indicators of cropland suitability, having a high correlation to crop health, yield and biomass in previous studies. A variety of climate, soil, topography and vegetation covariates were used to establish a relationship with the training samples, with a total of 119 covariates being utilized per yearly suitability assessment. Random forest (RF) produced a superior prediction accuracy compared to support vector machine (SVM), having the mean overall accuracy of 76.6% to 68.1% for Subset A and 80.6% to 79.5% for Subset B. The 6.1% of the highly suitable FAO suitability class for soybean cultivation was determined on the sparsely utilized Subset A, while the intensively cultivated agricultural land produced only 1.5% of the same suitability class in Subset B. The applicability of the proposed method for other crop types adjusted by their respective vegetation periods, as well as the upgrade to high-resolution Sentinel-2 images, will be a subject of future research. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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13 pages, 3849 KiB

Open AccessEditor’s ChoiceArticle

Estimating Farm Wheat Yields from NDVI and Meteorological Data

by Astrid Vannoppen and Anne Gobin

Agronomy 2021, 11(5), 946; https://doi.org/10.3390/agronomy11050946 - 11 May 2021

Cited by 28 | Viewed by 4342

Abstract

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R² = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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28 pages, 13634 KiB

Open AccessEditor’s ChoiceArticle

UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits

by Farrah Melissa Muharam, Khairudin Nurulhuda, Zed Zulkafli, Mohamad Arif Tarmizi, Asniyani Nur Haidar Abdullah, Muhamad Faiz Che Hashim, Siti Najja Mohd Zad, Derraz Radhwane and Mohd Razi Ismail

Agronomy 2021, 11(5), 915; https://doi.org/10.3390/agronomy11050915 - 7 May 2021

Cited by 28 | Viewed by 3716

Abstract

Rapid, accurate and inexpensive methods are required to analyze plant traits throughout all crop growth stages for plant phenotyping. Few studies have comprehensively evaluated plant traits from multispectral cameras onboard UAV platforms. Additionally, machine learning algorithms tend to over- or underfit data and limited attention has been paid to optimizing their performance through an ensemble learning approach. This study aims to (1) comprehensively evaluate twelve rice plant traits estimated from aerial unmanned vehicle (UAV)-based multispectral images and (2) introduce Random Forest AdaBoost (RFA) algorithms as an optimization approach for estimating plant traits. The approach was tested based on a farmer’s field in Terengganu, Malaysia, for the off-season from February to June 2018, involving five rice cultivars and three nitrogen (N) rates. Four bands, thirteen indices and Random Forest-AdaBoost (RFA) regression models were evaluated against the twelve plant traits according to the growth stages. Among the plant traits, plant height, green leaf and storage organ biomass, and foliar nitrogen (N) content were estimated well, with a coefficient of determination (R²) above 0.80. In comparing the bands and indices, red, Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Red-Edge Wide Dynamic Range Vegetation Index (REWDRVI) and Red-Edge Soil Adjusted Vegetation Index (RESAVI) were remarkable in estimating all plant traits at tillering, booting and milking stages with R² values ranging from 0.80–0.99 and root mean square error (RMSE) values ranging from 0.04–0.22. Milking was found to be the best growth stage to conduct estimations of plant traits. In summary, our findings demonstrate that an ensemble learning approach can improve the accuracy as well as reduce under/overfitting in plant phenotyping algorithms. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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19 pages, 7304 KiB

Open AccessArticle

Maize Crop Coefficient Estimation Based on Spectral Vegetation Indices and Vegetation Cover Fraction Derived from UAV-Based Multispectral Images

by Mariana de Jesús Marcial-Pablo, Ronald Ernesto Ontiveros-Capurata, Sergio Iván Jiménez-Jiménez and Waldo Ojeda-Bustamante

Agronomy 2021, 11(4), 668; https://doi.org/10.3390/agronomy11040668 - 1 Apr 2021

Cited by 12 | Viewed by 4200

Abstract

Remote sensing-based crop monitoring has evolved unprecedentedly to supply multispectral imagery with high spatial-temporal resolution for the assessment of crop evapotranspiration (ETc). Several methodologies have shown a high correlation between the Vegetation Indices (VIs) and the crop coefficient (Kc). This work analyzes the estimation of the crop coefficient (Kc) as a spectral function of the product of two variables: VIs and green vegetation cover fraction (f_v). Multispectral images from experimental maize plots were classified to separate pixels into three classes (vegetation, shade and soil) using the OBIA (Object Based Image Analysis) approach. Only vegetation pixels were used to estimate the VIs and f_v variables. The spectral Kc_fv:VI models were compared with Kc based on Cumulative Growing Degree Days (CGDD) (Kc-c_GDD). The maximum average values of Normalized Difference Vegetation Index (NDVI), WDRVI, amd EVI2 indices during the growing season were 0.77, 0.21, and 1.63, respectively. The results showed that the spectral Kc_fv:VI model showed a strong linear correlation with Kc-c_GDD (R² > 0.80). The model precision increases with plant densities, and the Kc_fv:NDVI with 80,000 plants/ha had the best fitting performance (R² = 0.94 and RMSE = 0.055). The results indicate that the use of spectral models to estimate Kc based on high spatial and temporal resolution UAV-images, using only green pixels to compute VI and f_v crop variables, offers a powerful and simple tool for ETc assessment to support irrigation scheduling in agricultural areas. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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16 pages, 1655 KiB

Open AccessEditor’s ChoiceArticle

Exploring Climate Change Effects on Vegetation Phenology by MOD13Q1 Data: The Piemonte Region Case Study in the Period 2001–2019

by Filippo Sarvia, Samuele De Petris and Enrico Borgogno-Mondino

Agronomy 2021, 11(3), 555; https://doi.org/10.3390/agronomy11030555 - 15 Mar 2021

Cited by 30 | Viewed by 3096

Abstract

Rising temperature, rainfall, and wind regime changes, increasing of frequency and intensity of extreme events are only some of the effects of climate change affecting the agro-forestry sector. Earth Observation data from satellite missions (often available for free) can certainly support analysis of climate change effects on vegetation, making possible to improve land management in space and time. Within this context, the present work aims at investigating natural and agricultural vegetation, as mapped by Corine Land Cover (CLC) dataset, focusing on phenological metrics trends that can be possibly conditioned by the ongoing climate-change. The study area consists of the entire Piemonte region (NW-Italy). MOD13Q1-v6 dataset from TERRA MODIS mission was used to describe pluri-annual (2001–2019) phenological behavior of vegetation focusing on the following CLC classes: Non-irrigated arable land, Vineyards, Pastures, and Forests. After computing and mapping some phenological metrics as derivable from the interpretation of at-pixel level NDVI (Normalized Difference Vegetation Index) temporal profile, we found that the most significant one was the maximum annual NDVI (MaxNDVI). Consequently, its trend was analyzed at CLC class level for the whole Piemonte region. Natural and semi-natural vegetation classes (Pastures and Forests) were furtherly investigated testing significance of the Percent Total Variation (TV%) of MaxNDVI in the period 2001–2019 for different altitude classes. Results proved that Non-irrigated arable land showed a not significant trend of MaxNDVI; differently, vineyards and forests showed a significant increasing one. Concerning TV%, it was found that it increases with altitude for the Forests CLC class, while it decreases with altitude for the pastures class. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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12 pages, 1634 KiB

Open AccessArticle

Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

by Ewa Panek and Dariusz Gozdowski

Agronomy 2021, 11(2), 340; https://doi.org/10.3390/agronomy11020340 - 14 Feb 2021

Cited by 22 | Viewed by 3084

Abstract

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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14 pages, 15612 KiB

Open AccessArticle

Mapping Permanent Gullies in an Agricultural Area Using Satellite Images: Efficacy of Machine Learning Algorithms

by Kwanele Phinzi, Imre Holb and Szilárd Szabó

Agronomy 2021, 11(2), 333; https://doi.org/10.3390/agronomy11020333 - 13 Feb 2021

Cited by 15 | Viewed by 3197

Abstract

Gullies are responsible for detaching massive volumes of productive soil, dissecting natural landscape and causing damages to infrastructure. Despite existing research, the gravity of the gully erosion problem underscores the urgent need for accurate mapping of gullies, a first but essential step toward sustainable management of soil resources. This study aims to obtain the spatial distribution of gullies through comparing various classifiers: k-dimensional tree K-Nearest Neighbor (k-d tree KNN), Minimum Distance (MD), Maximum Likelihood (ML), and Random Forest (RF). Results indicated that all the classifiers, with the exception of ML, achieved an overall accuracy (OA) of at least 0.85. RF had the highest OA (0.94), although it was outperformed in gully identification by MD (0% commission), but the omission error was 20% (MD). Accordingly, RF was considered as the best algorithm, having 13% error in both adding (commission) and omitting pixels as gullies. Thus, RF ensured a reliable outcome to map the spatial distribution of gullies. RF-derived gully density map reflected the agricultural areas most exposed to gully erosion. Our approach of using satellite imagery has certain limitations, and can be used only in arid or semiarid regions where gullies are not covered by dense vegetation as the vegetation biases the extracted gullies. The approach also provides a solution to the lack of laser scanned data, especially in the context of the study area, providing better accuracy and wider application possibilities. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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13 pages, 1302 KiB

Open AccessArticle

Estimating Soybean Radiation Use Efficiency Using a UAV in Iowa

by Xavier A. Phillips, Yuba R. Kandel, Mark A. Licht and Daren S. Mueller

Agronomy 2020, 10(12), 2002; https://doi.org/10.3390/agronomy10122002 - 20 Dec 2020

Cited by 3 | Viewed by 2838

Abstract

Radiation use efficiency (RUE) is difficult to estimate and unreasonable to perform on a small plot scale using traditional techniques. However, the increased availability of Unmanned Aerial Vehicles (UAVs) provides the ability to collect spatial and temporal data at high resolution and frequency, which has made a potential workaround. An experiment was completed in Iowa to (i) demonstrate RUE estimation of soybean [Glycine max (L.) Merr.] from reflectance data derived from consumer-grade UAV imagery and (ii) investigate the impact of foliar fungicides on RUE in Iowa. Some fungicides are promoted to have plant health benefits beyond disease protection, and changes in RUE may capture their effect. Frogeye leaf spot severity did not exceed 2%. RUE values ranged from 0.98 to 1.07 and 0.96 to 1.12 across the entire season and the period post-fungicide application, respectively, and fell within the range of previously published soybean RUE values. Plots treated with fluxapyroxad + pyraclostrobin had more canopy cover (p = 0.078) compared to the non-treated control 133 days after planting (DAP), but yields did not differ. A “greening effect” was detected at the end of the sample collection. RUE estimation using UAV imagery can be considered a viable option for the evaluation of management techniques on a small plot scale. Since it is directly related to yield, RUE could be an appropriate parameter to elucidate the impact of plant diseases and other stresses on yield. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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25 pages, 7996 KiB

Open AccessReview

Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

by Ali Ahmad, Javier Ordoñez, Pedro Cartujo and Vanesa Martos

Agronomy 2021, 11(1), 7; https://doi.org/10.3390/agronomy11010007 - 23 Dec 2020

Cited by 42 | Viewed by 8698

Abstract

The current COVID-19 global pandemic has amplified the pressure on the agriculture sector, inciting the need for sustainable agriculture more than ever. Thus, in this review, a sustainable perspective of the use of remotely piloted aircraft (RPA) or drone technology in the agriculture sector is discussed. Similarly, the types of cameras (multispectral, thermal, and visible), sensors, software, and platforms frequently deployed for ensuring precision agriculture for crop monitoring, disease detection, or even yield estimation are briefly discoursed. In this regard, vegetation indices (VIs) embrace an imperative prominence as they provide vital information for crop monitoring and decision-making, thus a summary of most commonly used VIs is also furnished and serves as a guide while planning to collect specific crop data. Furthermore, the establishment of significant applications of RPAs in livestock, forestry, crop monitoring, disease surveillance, irrigation, soil analysis, fertilization, crop harvest, weed management, mechanical pollination, crop insurance and tree plantation are cited in the light of currently available literature in this domain. RPA technology efficiency, cost and limitations are also considered based on the previous studies that may help to devise policies, technology adoption, investment, and research activities in this sphere. Full article

(This article belongs to the Special Issue Remote Sensing in Agriculture)

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