GobhiSet: Dataset of raw, manually, and automatically annotated RGB images across phenology of Brassica oleracea var. Botrytis

Rana, Shubham; Crimaldi, Mariano; Barretta, Domenico; Carillo, Petronia; Cirillo, Valerio; Maggio, Albino; Sarghini, Fabrizio; Gerbino, Salvatore

doi:10.1016/j.dib.2024.110506

Cited by 4 publications

(2 citation statements)

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“…For each temporal instance, an orthomosaic image was synthesized. The dataset comprises a total of 250 raw images, inclusive of 6 orthomosaic images [34]. The flight altitude exhibited a range from 4.275 m to 4.749 m Above Ground Level (AGL), influenced by atmospheric wind turbulence.…”

Section: Image Acquisitionmentioning

confidence: 99%

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Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics

Rana,

Gerbino,

Akbari Sekehravani

et al. 2024

Preprint

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Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and orthomosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis). The methodology employs YOLOv8, Grounding DINO, and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using initial datasets, which then facilitated the training of the Grounded SAM framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study's findings highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. Results indicate consistent and comparable growth patterns between aerial images and orthomosaics, with significant periods of rapid expansion and minor fluctuations over time. The integration of these advanced techniques demonstrates the potential for enhancing precision agriculture through reduced labor and increased accuracy in crop growth monitoring.

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Section: Image Acquisitionmentioning

confidence: 99%

“…The method of annotation involved the use of bounding boxes, and the resulting data were preserved in the COCO JSON format [25] as shown in Figure 3. The annotated information can be found in [34]:…”

Section: Manual Image Annotationsmentioning

confidence: 99%