Crop Growth Monitoring with Drone-Borne DInSAR

Oré, Gian; Alcântara, Maria Regina; Góes, Juliana A.; Oliveira, Luciano P.; Yepes, Jhonnatan; Teruel, Bárbara; Castro, Valquíria; Bins, Leonardo S.; Castro, Felicio; Luebeck, Dieter; Moreira, Laila F.; Gabrielli, Lucas H.; Hernandez-Figueroa, Hugo E.

doi:10.3390/rs12040615

Cited by 42 publications

(32 citation statements)

References 19 publications

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“…The cluster 'AFRICA' is related to works that mention the importance of coffee production for the African economy [60]. The cluster 'DIFFERENTIAL-INTERFEROMETRY' show the use of radar images for crop growth monitoring [77] as differential synthetic aperture radar interferometry (DInSAR) in Q2.…”

Section: Strategic Themes Related To Pa and A40 In The Coffee Sementioning

confidence: 99%

“…[81] present a proposal of IoT architecture to integrate sensors to monitoring the variables and information of the fields, this integration facilitates the management of the activities and resources. The A4.0 technologies also was used to evaluate the vegetation and soil through hyperspectral and computational processing of images [79], [102] through the use of drones, optical sensors and radars to create maps of crop growth and monitoring the characteristics of the plants [77].…”

Section: Internet Of Thingsmentioning

confidence: 99%

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Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

et al. 2020

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Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.

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Section: Strategic Themes Related To Pa and A40 In The Coffee Sementioning

confidence: 99%

Section: Internet Of Thingsmentioning

confidence: 99%

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

et al. 2020

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“…Thermal imaging was not considered in the reviewed articles. Recently, UAV-based methods that are based on ground-penetrating radar [108] and synthetic aperture radar [109] also began to emerge. An accurate knowledge of the moisture conditions could be exploited to improve the efficiency of pest and disease detection by targeting the detection at the parts of the field where pests and diseases are most likely to occur.…”

Section: Discussionmentioning

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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“…This method uses a Synthetic Aperture Radar (SAR) algorithm to reduce surface clusters and target detection, but it is impossible to detect objects with drones without vehicles. In Reference [15], the paper has studied the methodology of acquiring a growth deficit map with an accuracy of up to 5 cm and a spatial resolution of 1 m using Diffusion Interferometry-SAR (DIN-SAR). In order to form high-resolution maps for crop growth monitoring, satellite-based radar optical sensors and drone-borne optical sensors are necessary.…”

Section: Introductionmentioning

confidence: 99%

Improvement in Target Range Estimation and the Range Resolution Using Drone

Lee

2020

Electronics

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This study measured the speed of a moving vehicle in multiple lanes using a drone. The existing methods for measuring a vehicle’s speed while driving on the road measure the speed of moving automobiles by means of a sensor that is mounted on a structure. In another method, a person measures the speed of a vehicle at the edge of a road using a speed-measuring tool. The existing method for measuring a vehicle’s speed requires the installation of a gentry-structure; however, this produces a high risk for traffic accidents, which makes it impossible to measure a vehicle’s speed in multiple lanes at once. In this paper, a method that used a drone to measure the speed of moving vehicles in multiple lanes was proposed. The suggested method consisted of two LiDAR sets mounted on the drone, with each LiDAR sensor set measuring the speed of vehicles moving in one lane; that is, estimating the speed of moving vehicles in multiple lanes was possible by moving the drone over the road. The proposed method’s performance was compared with that of existing equipment in order to measure the speed of moving vehicles using the manufactured drone. The results of the experiment, in which the speed of moving vehicles was measured, showed that the Root Mean Square Error (RMSE) of the first lane and the second lane was 3.30 km/h and 2.27 km/h, respectively. The vehicle detection rate was 100% in the first lane. In the second lane, the vehicle detection rate was 94.12%, but the vehicle was not detected twice in the experiment. The average vehicle detection rate is 97.06%. Compared with the existing measurement system, the multi-lane moving vehicle speed measurement method that used the drone developed in this study reduced the risk of accidents, increased the convenience of movement, and measured the speed of vehicles moving in multiple lanes using a drone. In addition, it was more efficient than current measurement systems because it allowed an accurate measurement of speed in bad environmental conditions.

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Crop Growth Monitoring with Drone-Borne DInSAR

Cited by 42 publications

References 19 publications

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends

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

Improvement in Target Range Estimation and the Range Resolution Using Drone

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