An Effective Leaf Area Index Estimation Method for Wheat from UAV-Based Point Cloud Data

Song, Yang; Wang, Jinfei; Shan, Bo

doi:10.1109/igarss.2019.8899075

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The monitoring systems for leaves and also agricultural applications using aerial systems were mostly vision-based monitoring systems without interaction with the plant [26]- [28]. The problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades [29].…”

Section: Introductionmentioning

confidence: 99%

A 79.7g Manipulator Prototype for E-Flap Robot: A Plucking-Leaf Application

et al. 2022

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The manipulation capabilities of flapping-wing flying robots (FWFRs) is a problem barely studied. This is a direct consequence of the load-carrying capacity limitation of the flapping-wing robots. Ornithopters will improve the existent multirotor unmanned aerial vehicles (UAVs) since they could perform longer missions and offer a safe interaction in proximity to humans. This technology also opens the possibility to perch in some trees and perform tasks such as obtaining samples from nature, enabling biologists to collect samples in remote places, or assisting people in rescue missions by carrying medicines or first-aid kits. This paper presents a very lightweight manipulator (79.7g) prototype to be mounted on an ornithopter. The distribution of the mass on the flapping-wing robot is sensitive and an extra lumped mass far from the center-of-mass (CoM) of the robot deteriorates the flight stability. A configuration was proposed to avoid changing the CoM. Flight experiments show that adding the arm to the robot only moved the CoM 6mm and the performance of the flight with the manipulator has been satisfactory. Plucking leaf is chosen as an application to the designed system and several experimental tests confirmed successful sampling of leaves by the prototype.

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Section: Introductionmentioning

confidence: 99%

A 79.7g Manipulator Prototype for E-Flap Robot: A Plucking-Leaf Application

et al. 2022

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“…SfM is a computer vision technique that incorporates multi-view stereo images to match features, derive 3D structure, and estimate camera position and orientation. The SfM approach has been proven to be able to estimate of crop structural information such as, canopy size, height, and leave shape [19][20][21][22]. For example, Bendig (2014) [21] used a crop surface model to estimate the height of barley, highly correlated with the dry aboveground biomass.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Winter Wheat Yield from UAV-Based Multi-Temporal Imagery Using Crop Allometric Relationship and SAFY Model

Song

Wang

Shan

2021

Drones

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Crop yield prediction and estimation play essential roles in the precision crop management system. The Simple Algorithm for Yield Estimation (SAFY) has been applied to Unmanned Aerial Vehicle (UAV)-based data to provide high spatial yield prediction and estimation for winter wheat. However, this crop model relies on the relationship between crop leaf weight and biomass, which only considers the contribution of leaves on the final biomass and yield calculation. This study developed the modified SAFY-height model by incorporating an allometric relationship between ground-based measured crop height and biomass. A piecewise linear regression model is used to establish the relationship between crop height and biomass. The parameters of the modified SAFY-height model are calibrated using ground measurements. Then, the calibrated modified SAFY-height model is applied on the UAV-based photogrammetric point cloud derived crop height and effective leaf area index (LAIe) maps to predict winter wheat yield. The growing accumulated temperature turning points of an allometric relationship between crop height and biomass is 712 °C. The modified SAFY-height model, relative to traditional SAFY, provided more accurate yield estimation for areas with LAI higher than 1.01 m2/m2. The RMSE and RRMSE are improved by 3.3% and 0.5%, respectively.

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“…User demands are growing both for the quality of the modeling and the final resolution. UAVs are used in many areas where visual spectrum images or multi-spectral images, digital surface models (DSMs), and orthoimagery are derived and encompass the following fields: geodesy [6][7][8][9][10][11][12][13][14][15], agriculture [14,[16][17][18][19], forestry [20][21][22], archaeology and architecture [10,[23][24][25][26][27], environment and technical infrastructure monitoring [6,7,11,[17][18][19]21,[28][29][30][31][32][33], and emergency management and traffic monitoring [34][35][36]. Numerous cases of UAV applications have been realized by the author, during which, some problems have been encountered [37].…”

Section: Introductionmentioning

confidence: 99%

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

Burdziakowski

2020

Remote Sensing

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Unmanned aerial vehicles (UAVs) have now become very popular in photogrammetric and remote-sensing applications. Every day, these vehicles are used in new applications, new terrains, and new tasks, facing new problems. One of these problems is connected with flight altitude and the determined ground sample distance in a specific area, especially within cities and industrial and construction areas. The problem is that a safe flight altitude and camera parameters do not meet the required or demanded ground sampling distance or the geometrical and texture quality. In the cases where the flight level cannot be reduced and there is no technical ability to change the UAV camera or lens, the author proposes the use of a super-resolution algorithm for enhancing images acquired by UAVs and, consequently, increase the geometrical and interpretation quality of the final photogrammetric product. The main study objective was to utilize super-resolution (SR) algorithms to improve the geometric and interpretative quality of the final photogrammetric product, assess its impact on the accuracy of the photogrammetric processing and on the traditional digital photogrammetry workflow. The research concept assumes a comparative analysis of photogrammetric products obtained on the basis of data collected from small, commercial UAVs and products obtained from the same data but additionally processed by the super-resolution algorithm. As the study concludes, the photogrammetric products that are created as a result of the algorithms’ operation on high-altitude images show a comparable quality to the reference products from low altitudes and, in some cases, even improve their quality.

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An Effective Leaf Area Index Estimation Method for Wheat from UAV-Based Point Cloud Data

Cited by 5 publications

References 8 publications

A 79.7g Manipulator Prototype for E-Flap Robot: A Plucking-Leaf Application

A 79.7g Manipulator Prototype for E-Flap Robot: A Plucking-Leaf Application

Estimation of Winter Wheat Yield from UAV-Based Multi-Temporal Imagery Using Crop Allometric Relationship and SAFY Model

Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products using Super-Resolution Algorithms

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