Biology, ecology and management of Raphanus raphanistrum L.: a noxious agricultural and environmental weed

Kebaso, Lynda; Frimpong, David; Iqbal, Nadeem; Bajwa, Ali Ahsan; Namubiru, Halima; Ali, Hafiz Haider; Ramiz, Zarka; Hashim, Saima; Manalil, Sudheesh; Chauhan, Bhagirath Singh

doi:10.1007/s11356-020-08334-x

Cited by 7 publications

(2 citation statements)

References 62 publications

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“…Its natural habitats are in western Asia, Europe, and some regions of Northern Africa. This species has been spread to all corners of the globe and is often considered a harmful invasive species due to its impact on local habitats [ [2] , [3] , [4] ]. Hence, this openly accessible annotated MS dataset for R. raphanistrum highlighted here, encompasses data from both manually and automatically annotated wild radish leaves along with the ground truth ROI.…”

Section: Value Of the Datamentioning

confidence: 99%

RafanoSet: Dataset of raw, manually, and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation.

Rana,

Gerbino,

Barretta

et al. 2024

Data in Brief

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Section: Value Of the Datamentioning

confidence: 99%

RafanoSet: Dataset of raw, manually, and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation.

Rana,

Gerbino,

Barretta

et al. 2024

Data in Brief

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“…Another experiment based on Parrot Sequoia MS images performed co-registration without assumptions based on scene structure and just required dense matching between two spectrally similar channels [ 7 ]. The weed Raphanus Raphanistrum , also known as wild radish, belongs to the family of Brassicaceae , whose infestation in the cropping of wheat is a subject of research interest [ 25 , 26 ]. Wild radish poses a significant threat to winter crops, including wheat, as it is one of the most aggressive and competitive broad-leaved weeds.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment

Rana,

Gerbino,

Crimaldi

et al. 2024

J. Imaging

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This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.

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Raphanus raphanistrum L.Raphanus sativus L.Brassicaceae

Ouasti,

Bussmann,

Elachouri

2023

Ethnobotany of Mountain Regions

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Biology, ecology and management of Raphanus raphanistrum L.: a noxious agricultural and environmental weed

Cited by 7 publications

References 62 publications

RafanoSet: Dataset of raw, manually, and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation.

RafanoSet: Dataset of raw, manually, and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation.

Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment

Raphanus raphanistrum L.Raphanus sativus L.Brassicaceae

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