Cargando…
Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter
The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop dise...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534855/ https://www.ncbi.nlm.nih.gov/pubmed/37765940 http://dx.doi.org/10.3390/s23187884 |
_version_ | 1785112492473581568 |
---|---|
author | Li, Yaqin Wang, Dandan Yuan, Cao Li, Hao Hu, Jing |
author_facet | Li, Yaqin Wang, Dandan Yuan, Cao Li, Hao Hu, Jing |
author_sort | Li, Yaqin |
collection | PubMed |
description | The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique. By leveraging the distinctive characteristics of agricultural image segmentation and suitable user prompts, the model enables zero-shot segmentation, providing a new approach for zero-sample image segmentation in the agricultural domain. Comprehensive experiments are conducted to assess the efficacy of the ASA compared to the default SAM. The results show that the proposed model achieves significant improvements on all 12 agricultural segmentation tasks. Notably, the average Dice score improved by 41.48% on two coffee-leaf-disease segmentation tasks. |
format | Online Article Text |
id | pubmed-10534855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105348552023-09-29 Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter Li, Yaqin Wang, Dandan Yuan, Cao Li, Hao Hu, Jing Sensors (Basel) Article The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique. By leveraging the distinctive characteristics of agricultural image segmentation and suitable user prompts, the model enables zero-shot segmentation, providing a new approach for zero-sample image segmentation in the agricultural domain. Comprehensive experiments are conducted to assess the efficacy of the ASA compared to the default SAM. The results show that the proposed model achieves significant improvements on all 12 agricultural segmentation tasks. Notably, the average Dice score improved by 41.48% on two coffee-leaf-disease segmentation tasks. MDPI 2023-09-14 /pmc/articles/PMC10534855/ /pubmed/37765940 http://dx.doi.org/10.3390/s23187884 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Yaqin Wang, Dandan Yuan, Cao Li, Hao Hu, Jing Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter |
title | Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter |
title_full | Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter |
title_fullStr | Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter |
title_full_unstemmed | Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter |
title_short | Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter |
title_sort | enhancing agricultural image segmentation with an agricultural segment anything model adapter |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534855/ https://www.ncbi.nlm.nih.gov/pubmed/37765940 http://dx.doi.org/10.3390/s23187884 |
work_keys_str_mv | AT liyaqin enhancingagriculturalimagesegmentationwithanagriculturalsegmentanythingmodeladapter AT wangdandan enhancingagriculturalimagesegmentationwithanagriculturalsegmentanythingmodeladapter AT yuancao enhancingagriculturalimagesegmentationwithanagriculturalsegmentanythingmodeladapter AT lihao enhancingagriculturalimagesegmentationwithanagriculturalsegmentanythingmodeladapter AT hujing enhancingagriculturalimagesegmentationwithanagriculturalsegmentanythingmodeladapter |