Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

Jiang, Hao; Chen, Shuisen; Li, Dan; Wang, Chongyang; Yang, Ji

doi:10.3390/rs9070721

Cited by 35 publications

(15 citation statements)

References 17 publications

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“…They achieved an overall 90% detection rate, and a 40 cm positional error for individual trees. Jiang et al (2017) used scalespace filtering on a papaya and a lemon orchard with isolated individual trees from images obtained by unmanned aerial vehicles (UAV). Applying the Laplacian of the Gaussian function resulted in a 95% detection rate for trees.…”

Section: Modelsmentioning

confidence: 99%

Scaling of remote sensing information for orchard management

Mahour¹

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After an exciting period of working and doing research at ITC Faculty, today is the day: writing this note of appreciation is the finishing touch on my PhD research. It has been a time of profound learning for me, not only in the scientific arena but also on a personal level. Doing this research has had a significant impact on me. I would like to deliberate on the people who have helped and encouraged me so much throughout this period. I would first like to express my special appreciations to my promoter Prof. Alfred Stein for his advice, support and continuous encouragements have contributed significantly to the completion of this research. I would remarkably like to single out my supervisor Dr. Valentyn Tolpekin: I want to thank you for your excellent assistance and for all of the opportunities I was given to conduct my research. I would like to extend my appreciation to Dr. Ali Sharifi for his support, advice and critical comments. I would also like to thank my family for the love of my life my loving mother Fariba, my father Jamshid, my brothers Mohamadamin and Amirali, and my lovely grandma, thank you for giving me a stable mind to think and work for science. I am thankful to you for all what you have done for me. Because of you, I am standing here.

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Section: Modelsmentioning

confidence: 99%

Scaling of remote sensing information for orchard management

Mahour¹

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“…CUDA exposes a two-level thread hierarchy 14 to offer developers an optimization method. All threads are divided into blocks, which are grouped into grids [46]. A kernel is executed by being a grid.…”

Section: Implementation Of the Conformal Symplectic Partitionedmentioning

confidence: 99%

Analysis of GPR Wave Propagation Using CUDA‐Implemented Conformal Symplectic Partitioned Runge‐Kutta Method

2019

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Accurate forward modeling is of great significance for improving the accuracy and speed of inversion. For forward modeling of large sizes and fine structures, numerical accuracy and computational efficiency are not high, due to the stability conditions and the dense grid number. In this paper, the symplectic partitioned Runge-Kutta (SPRK) method, surface conformal technique, and graphics processor unit (GPU) acceleration technique are combined to establish a precise and efficient numerical model of electromagnetic wave propagation in complex geoelectric structures, with the goal of realizing a refined and efficient calculation of the electromagnetic response of an arbitrarily shaped underground target. The results show that the accuracy and efficiency of ground-penetrating radar (GPR) forward modeling are greatly improved when using our algorithm. This provides a theoretical basis for accurately interpreting GPR detection data and accurate and efficient forward modeling for the next step of inversion imaging.

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“…For example, Malek, et al [31] identified palm trees from UAV images using an extreme learning machine classifier. Jiang, et al [32] proposed an improved scale-space filtering algorithm in a GPU-environment for papaya tree detection, achieving high accuracies with an increase in the speed of computation. In an agrosilvopastoral environment, Surový, et al [33] successfully estimated position and heights of tree plantations based on a 3D point cloud and orthophoto derived from UAV images.…”

Section: Introductionmentioning

confidence: 99%

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

Csillik

Cherbini²,

Johnson

et al. 2018

Drones

173

116

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Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.

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Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

Cited by 35 publications

References 17 publications

Scaling of remote sensing information for orchard management

Scaling of remote sensing information for orchard management

Analysis of GPR Wave Propagation Using CUDA‐Implemented Conformal Symplectic Partitioned Runge‐Kutta Method

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

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