A research team demonstrated that the Segment Anything Model (SAM) accurately identifies individual berries in 2D grape cluster images, achieving a strong correlation with human-identified berries (Pearson’s r² = 0.96). This method generated over 150,000 berry masks from approximately 3,500 images. The results show the potential for integrating SAM into existing vineyard image-processing pipelines to improve cluster architecture and compactness analysis. Future applications include enhanced vineyard management and breeding practices by providing precise berry count and spatial information.
Grape cluster architecture and compactness significantly impact yield, quality, and disease susceptibility. These traits are complex, influenced by factors like berry size and arrangement, and are challenging to measure accurately. Current methods, such as visual scoring and computer vision, have limitations in precision and scalability.
To address these issues, a study (DOI: 10.34133/plantphenomics.0202) published in Plant Phenomics on 27 Jun 2024, aims to utilize the Segment Anything Model (SAM) to segment grape berries in 2D images without additional training, thereby enhancing the accuracy and efficiency of analyzing cluster architecture and compactness for improved vineyard management and breeding programs.
The study utilized the SAM algorithm on a population of 387 vines and 1,935 clusters, generating 215,090 masks. For 99 vines, clusters were imaged at four angles, resulting in 3,431 images. The algorithm identified various objects, filtering out 55,550 masks of overlapping or improperly sized berries, leaving 153,939 true berry masks. The average berry count per cluster was 44.87, with counts normally distributed. Processing time varied with grid density; a 32x32 grid took 55 seconds per image on a CPU and 14 seconds on a GPU. Increasing to 62x62 points increased processing time to 4 minutes and 45 seconds. Berry counts from cluster images showed a high correlation with manual counts (R2 = 0.93) but were underestimated by about 50%. This underestimation was consistent and correctable with linear regression, improving accuracy to an adjusted R2 of 0.8723. Berry size predictions were more variable but also linearly adjustable (adjusted R2 = 0.8457). Imaging angle significantly impacted berry count predictions, especially for clusters with asymmetries, while berry size was less affected. The methodology demonstrated sensitivity to cluster architectural features and genetic variance, with consistent repeatability for traits like berry count and cluster compactness.
According to the study's lead researcher, Diaz-Garcia, “We emphasized the critical importance of the angle at which the cluster is imaged, noting its substantial effect on berry counts and architecture. We proposed different approaches in which berry location information facilitated the calculation of complex features related to cluster architecture and compactness. Finally, we discussed SAM’s potential integration into currently available pipelines for image generation and processing in vineyard conditions.”
In summary, this study used the SAM algorithm to accurately segment grape berries in 2D cluster images, correcting a 50% underestimation using linear regression (adjusted R² = 0.87). The findings highlight SAM's potential for precise, scalable cluster analysis in vineyard management and breeding programs.
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References
DOI
10.34133/plantphenomics.0202
Original Source URL
https://doi.org/10.34133/plantphenomics.0202
Authors
Efrain Torres-Lomas 1 , Jimena Lado-Bega 2, Guillermo Garcia-Zamora 1,and Luis Diaz-Garcia 1*
Affications
1 Department of Viticulture and Enology, University of California Davis, Davis, CA 95616, USA.
2 Soil andWater Department, Universidad de la Republica, Montevideo 11400, Uruguay.
Funding information
This project was partially supported by USDA-NIFA Specialty Crop Research Initiative Award No. 2022-51181-38240.
About Plant Phenomics
Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.