UAV applications in Agriculture 4.0

Amaral, Lucas Rios do; Zerbato, Cristiano; Freitas, Rodrigo Greggio de; Júnior, Marcelo Rodrigues Barbosa; Simões, Isabela Ordine Pires da Silva

doi:10.5935/1806-6690.20200091

Cited by 12 publications

(3 citation statements)

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“…Where traditional satellite sensing is still unsatisfactory, but UAV imaging could improve or even replace routine data assessments. In recent years, Data science has advanced significantly [24]. This technology is used in agriculture to help monitor crops from afar, making it easier to make decisions about the state of the field.…”

Section: Application Of Uav In Agriculturementioning

confidence: 99%

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Sehree

Khidhir²

2022

IJEECS

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Unmanned aerial vehicles (UAVs) are one of the various aerial remote sensing platforms with ease of use and cost-effectiveness it can deliver high-resolution imaging, obtained using a variety of sensors. Photogrammetric data is derived by the use of unmanned aerial systems (UAS, which consists of a UAV, sensor(s), and base station). As a result of these types, vegetation monitoring is conceivable. Deep neural networks have had a lot of success with image classification tasks, especially in the remote sensing field. In this paper, we demonstrate how deep neural networks can be used to classify olive trees status from aerial images. We have addressed a multi-class classification problem. In this work five different neural network architectures: VGG16, ResNet50, MobileNet, Xception, and VGG19 had been compared. Transfer learning had been accomplished using training of the fully connected layer(s) at the end of the deep learning layers. We used metrics such as accuracy, precision, recall, and confusion metric to evaluate the results. With accuracy, our model achieves the best results using ResNet50 with an accuracy is (97.2%).

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Section: Application Of Uav In Agriculturementioning

confidence: 99%

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Sehree

Khidhir²

2022

IJEECS

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“…UAVs also reduce the no-land flight rate due to lower labor intensity and lower operational costs (del Cerro et al, 2021). The applications of UAV in physiological and biophysical assessments, spraying of phytosanitary, monitoring the biological targets, and topographic surveys were studied by Amaral et al (2020). The author concluded that the UAV could replace human activities in agriculture, like crop monitoring phytosanitary and spraying pesticides.…”

Section: Introductionmentioning

confidence: 99%

Energy Industry and Industrial Design (Innovations in Energy Utilization and Equipment Design)

2024

JST

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This study represents the aerodynamic design of an Unmanned aerial vehicle intended for surveillance or agriculture with a maximum take weight of 125 kg. Weight estimation and constraint analysis were done based on the Mission profile. Design of Computer-Aided Design (CAD) models were generated for three different configurations using CATIA V5R20 as a high wing, mid-wing, and low wing. Flow analysis was done for the above configurations at various angles of attack. ANSYS 15 was used for the flow Analysis. A Tetrahedron element meshed the model with the minimum required orthogonal quality. Five microns were given to the initial layer height of the prism mesh. Spalart Allmaras model is used as the Turbulence model in the solver. The aerodynamic characteristics of the above configuration obtained from Computational Fluid Dynamics (CFD) results were compared with the DATCOM program and validated with the wind tunnel experimental test data. The open-circuited suction-type Subsonic wind tunnel was employed for the test. The aerodynamic properties for the angle of attack in the range of -2° to 14° angle of attack are calculated using a six-component balance.The study aims to find the Unmanned Aerial Vehicle (UAV) configuration based on the aerodynamic characteristics obtained from the CFD and DATCOM results. High-wing UAVs have better aerodynamic efficiency than the other two configurations.

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“…UAVs are the next generation of data-acquisition platforms for agriculture. They are able to capture information about an object with increased richness of details than satellites [6][7][8][9][10][11]. Moreover, they are not dependent on weather (e.g., cloudiness) as orbital sensors.…”

Section: Introductionmentioning

confidence: 99%

Mapping Gaps in Sugarcane by UAV RGB Imagery: The Lower and Earlier the Flight, the More Accurate

et al. 2021

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Imagery data prove useful for mapping gaps in sugarcane. However, if the quality of data is poor or the moment of flying an aerial platform is not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how the combination of pixel size (3.5, 6.0 and 8.2 cm) and height of plant (0.5, 0.9, 1.0, 1.2 and 1.7 m) could impact the mapping of gaps on unmanned aerial vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger the pixel or plant, the less accurate the prediction. Error was more likely to occur for regions on the field where actively growing vegetation overlapped at gaps of 0.5 m. Hence, even 3.5 cm pixel did not capture them. Overall, pixels of 3.5 cm and plants of 0.5 m outstripped other combinations, making it the most accurate (absolute error ~0.015 m) solution for remote mapping on the field. Our insights are timely and provide forward knowledge that is particularly relevant to progress in the field’s prominence of flying a UAV to map gaps. They will enable producers to make decisions on replanting and fertilizing site-specific high-resolution imagery data.

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UAV applications in Agriculture 4.0

Cited by 12 publications

References 0 publications

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Olive trees cases classification based on deep convolutional neural network from unmanned aerial vehicle imagery

Energy Industry and Industrial Design (Innovations in Energy Utilization and Equipment Design)

Mapping Gaps in Sugarcane by UAV RGB Imagery: The Lower and Earlier the Flight, the More Accurate

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