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210 results for “Raphanus raphanistrum”

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zenodo40/100

RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment

This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level..  The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops. The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number> _1: Blue _2: Green _3: Red _4: Near Infrared _5: RedEdge Example: An image name  IMG_0200_3 represents the scene number 200 in Red channel This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format. The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article.  Field experimentation roles: The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy. Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'.  Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.

opencc-by-4.0Jan 2024View details →

zenodo40/100

Figure 2 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait

Figure 2. The relationship between the number of semidominant larger M1 alleles and days to first flower (DFF), needed to model the long-day selections. Note that six smallereffect M2 genes will add another 0 to 18 days to DFF. If each M1 allele added the same amount in the long-day selections, the relationship would be linear.

opencc-by-4.0Jan 2024View details →

zenodo40/100

Figure 1 in Adaptations in wild radish (Raphanus raphanistrum) flowering time, Part 1: Individual-based modeling of a polygenic trait

Figure 1. Goodness of fit of the modeled data (A) when compared with the recorded glasshouse data (B). Graphs show cumulative days to first flowering (DFF) adaptations in Raphanus raphanistrum populations as a result of repeated early and late days to flowering selection. In both graphs, the basal population is the black solid line in the center,the darker lines are the matched generations of early flowering (EF1, EF3, FE4,and EF5; colored orange), and late flowering (LF2 and LF3; colored purple), with and late flowering (LF2 and LF3),with the two unmatched generations (EF2 and LF2) shown in lighter tones.The far-left population (early flowering EF5) displays very little phenotypic variability, whereas the far-right population (late flowering 3) is very diverse.

opencc-by-4.0Jan 2024View details →

zenodo40/100