Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes - A systematic review

Librán‐Embid, Felipe; Klaus, Felix; Tscharntke, Teja; Graß, Ingo

doi:10.1016/j.scitotenv.2020.139204

Cited by 87 publications

(45 citation statements)

References 209 publications

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“…For the field scale, grid sampling was adopted as the main strategy according to the previous studies [74]. In April 2018, 70 points were selected through sampling cells (0.5 m × 0.5 m) for uniform data collection, in which the surveys were executed simultaneously via UAVs.…”

Section: Soil Moisture Content Measurementmentioning

confidence: 99%

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Ding

Jin

et al. 2021

Remote Sensing

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Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

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Section: Soil Moisture Content Measurementmentioning

confidence: 99%

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Ding

Jin

et al. 2021

Remote Sensing

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“…The cluster 'UAV' (Figure 4 (a)) presents prominent importance in the strategic diagram (Figure 3) due to its high degree of interaction with the other motor themes. The UAV, commonly known as a drone [76], corresponds to a pilotless aircraft, i.e. a flying machine operated with no humans (passenger/pilot) onboard [77].…”

Section: Unmanned Aerial Vehicle (Uav)mentioning

confidence: 99%

“…UAVs are low-cost and easy-to-operate technologies in agriculture [78] that can be adopted for spraying in fields [79], such as water precision irrigation. Given the technological advancement emerging in different countries, UAVs have been gaining more and more space and importance for smart farming [76,78]. Different sensors can be coupled to UAVs [76], which demonstrates the strong relationship between UAV and the subtheme 'REMOTE-SENSING', followed by the subthemes 'MACHINE-LEARNING', 'VEGETATION-INDICES' and 'DRONE', among others (Figure 4 (a)).…”

Section: Unmanned Aerial Vehicle (Uav)mentioning

confidence: 99%

Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities

Mk¹,

LdS²,

Cr³

et al. 2021

Preprint

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: The agriculture sector is one of the backbones of many countries’ economies and its processes have been changing in order to enable technological adoption to increase productivity, quality, and sustainable development. In this research, we present a theoretical reflection through a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, the so-called Precision Agriculture (PA) and Agriculture 4.0 (A4.0). To do this, we used 4,694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis (BPNA) of the literature with the support of the PICOC protocol and the SciMAT software. Our findings present 22 strategic themes related to Digital Agriculture (DA) such as Internet of Things (IoT) and Climate-smart Agriculture (CSA) among others, and the thematic network structures of the motor themes and the thematic evolution structure of the field of the study over time. In addition, our results discuss the main challenges and opportunities of DA. Our findings have the potential to provide insights for practitioners and researchers in decision-making and pave the way for future works.

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“…[1] reviewed platforms, controls, and applications, while the authors in [6,7] focused on the applications and updated an earlier study [8]. The review in [9] had a biodiversity approach. To our knowledge, no review studies focused on the reference data for UAVs in the agricultural context.…”

Section: Introductionmentioning

confidence: 99%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes - A systematic review

Cited by 87 publications

References 209 publications

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

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