Developing an extreme learning machine based approach to weed segmentation in pastures

Ford, Jonathan; Sadgrove, Edmund J.; Planchard, David

doi:10.1016/j.atech.2023.100288

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…Thus, a first stage is carried out, in which vegetation in the image is detected by calculating a vegetation index and generating a vegetation mask through thresholding. For instance, to separate vegetation images from soil images, the Normalized Difference Vegetation Index (NDVI) is used when near-infrared (NIR) information is available [46]. Another possibility would be the use of hyperspectral images as they provide fine details on the spectral reflectance of each image pixel [15].…”

Section: Discussionmentioning

confidence: 99%

Initial Growth of Gmelina arborea and Efficacy of RGB Image to Capture Canopy Area in a Large Range of Stockings

Hakamada,

Prados-Coronado,

Lages

et al. 2023

Remote Sensing

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At present, there is a high demand for carbon (C) sequestration alternatives; thus, understanding tree growth and the efficacy of remote sensing techniques to capture forest plantation ecophysiology is crucial. This study evaluated the effect of contrasting stockings of Gmelina arborea on its initial growth and aboveground Carbon stock, and the efficacy of aerial images obtained using drones to capture the crown cover at different stockings. The results indicated that denser stockings showed greater tree heights and stem diameter increments, contrary to traditional measurements. The C storage capacity of Gmelina arborea was promising, with an aboveground estimated C stock of about 13 Mg ha−1 in 9 months, making it a valuable and promising species for CO2 sequestration under the context of climate change. The use of simple Red-Green-Blue (RGB) cameras and drones to detect and estimate crown areas in young plantations was mainly viable within the commercial range of stockings (500–2000 trees ha−1), and can be used as a powerful tool to better understand tree initial growth. The results showed effective discrimination without weeds independently of the stocking level; however, when weeds were present, the effectiveness decreased. This research provides valuable insights into forest management and improves the understanding of the silviculture behavior of a potential native species for reforestation in the tropics.

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Section: Discussionmentioning

confidence: 99%

Initial Growth of Gmelina arborea and Efficacy of RGB Image to Capture Canopy Area in a Large Range of Stockings

Hakamada,

Prados-Coronado,

Lages

et al. 2023

Remote Sensing

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Network intrusion detection utilizing information entropy measurement-based LLE and weighted kernel extreme learning machine with CPSO

Yao,

Chen,

Zhang

2023

Preprint

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In this study, network intrusion detection method of information entropy measurement-based LLE and weighted kernel extreme learning machine with CPSO (IEMLLE-CWKELM) is proposed for network intrusion detection. First of all, this article proposes an information entropy measurement-based locally linear embedding (IEMLLE) algorithm to reduce the features of network intrusion data. The IEMLLE algorithm is a dimensionality reduction algorithm based on information entropy measurement. The discrimination of the distribution of sample data of the different classes based on IEMLLE is higher than that based on locally linear embedding (LLE) algorithm. Moreover, this article proposes a weighted kernel extreme learning machine (CWKELM) algorithm, among which the use of kernel functions instead of hidden layer random feature maps containing activation functions is beneficial for improving the nonlinear processing ability and robustness of weighted extreme learning machine, and the chaos particle swarm optimization (CPSO) algorithm is proposed to optimize the penalty factor and the kernel parameter of weighted kernel extreme learning machine. The experimental results show that IEMLLE-CWKELM is the higher network intrusion detection accuracy than LLE-CWKELM, LLE-ELM, and principal component analysis- extreme learning machine (PCA-ELM).

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Latest Trends and Challenges in Digital Agriculture for Crop Production

Fuentes-Peñailillo,

Gutter,

Vega

et al. 2023

Preprint

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Developing an extreme learning machine based approach to weed segmentation in pastures

Cited by 4 publications

References 46 publications

Initial Growth of Gmelina arborea and Efficacy of RGB Image to Capture Canopy Area in a Large Range of Stockings

Initial Growth of Gmelina arborea and Efficacy of RGB Image to Capture Canopy Area in a Large Range of Stockings

Network intrusion detection utilizing information entropy measurement-based LLE and weighted kernel extreme learning machine with CPSO

Latest Trends and Challenges in Digital Agriculture for Crop Production

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