Version 1
: Received: 11 June 2024 / Approved: 11 June 2024 / Online: 12 June 2024 (09:05:34 CEST)
How to cite:
Rana, S.; Gerbino, S.; Akbari Sekehravani, E.; Russo, M. B.; Carillo, P. Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics. Preprints2024, 2024060740. https://doi.org/10.20944/preprints202406.0740.v1
Rana, S.; Gerbino, S.; Akbari Sekehravani, E.; Russo, M. B.; Carillo, P. Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics. Preprints 2024, 2024060740. https://doi.org/10.20944/preprints202406.0740.v1
Rana, S.; Gerbino, S.; Akbari Sekehravani, E.; Russo, M. B.; Carillo, P. Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics. Preprints2024, 2024060740. https://doi.org/10.20944/preprints202406.0740.v1
APA Style
Rana, S., Gerbino, S., Akbari Sekehravani, E., Russo, M. B., & Carillo, P. (2024). Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics. Preprints. https://doi.org/10.20944/preprints202406.0740.v1
Chicago/Turabian Style
Rana, S., Mario Brandon Russo and Petronia Carillo. 2024 "Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-date Aerial Images and Orthomosaics" Preprints. https://doi.org/10.20944/preprints202406.0740.v1
Abstract
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.
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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