No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level
Traditional alfalfa stem phenotyping is labor-intensive and susceptible to bias from subjective ratings. Computer vision and machine learning present a promising solution for objectively assessing stem morphology. This study proposed an AI-driven image analysis to replace manual phenotyping methods...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500509X |
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| author | Brandon J. Weihs Zhou Tang Somshubhra Roy Zezhong Tian Deborah Jo Heuschele Zhiwu Zhang Cranos Williams Zhou Zhang Garett Heineck Swayamjit Saha Zhanyou Xu |
| author_facet | Brandon J. Weihs Zhou Tang Somshubhra Roy Zezhong Tian Deborah Jo Heuschele Zhiwu Zhang Cranos Williams Zhou Zhang Garett Heineck Swayamjit Saha Zhanyou Xu |
| author_sort | Brandon J. Weihs |
| collection | DOAJ |
| description | Traditional alfalfa stem phenotyping is labor-intensive and susceptible to bias from subjective ratings. Computer vision and machine learning present a promising solution for objectively assessing stem morphology. This study proposed an AI-driven image analysis to replace manual phenotyping methods with high efficiency and accuracy. We developed a novel pipeline that combines YOLOv8n with Otsu’s thresholding and K-means clustering to identify the medoids of internal and external polygons of the stem, thereby quantifying stem traits using pixel-based morphometric masks. The approach achieved an F1 score of 0.91 in detecting and classifying hollow or solid stems across plots and genotypes. Further analysis measured stem area and the proportions of stem tissue versus hollow regions, generating traits like hollowness score and percentage of hollowness. These stem-level metrics provide novel, objective, and quantitative phenotypic measurements, supporting ongoing chemical digestibility analyses and enabling real-time, image-based digestibility assessments through a field-deployable mobile application. |
| format | Article |
| id | doaj-art-8b677a510cb3470d8301db2ce631ea1f |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-8b677a510cb3470d8301db2ce631ea1f2025-08-20T02:58:03ZengElsevierSmart Agricultural Technology2772-37552025-12-011210127810.1016/j.atech.2025.101278No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel levelBrandon J. Weihs0Zhou Tang1Somshubhra Roy2Zezhong Tian3Deborah Jo Heuschele4Zhiwu Zhang5Cranos Williams6Zhou Zhang7Garett Heineck8Swayamjit Saha9Zhanyou Xu10United States Department of Agriculture – Agricultural Research Service - Plant Science, Research, St. Paul, MN 55108, USA; Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA; Department of Humanities and Social Sciences, Northwest Missouri State University, Maryville, MO 64468, USA; Daugherty Water for Food Global Institute, University of Nebraska, Lincoln, NE 68588, USADepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; Department of Agronomy, University of Florida, Gainesville, FL 32611, USADepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USADepartment of Biological Systems Engineering, University of Wisconsin, Madison, WI 53706, USAUnited States Department of Agriculture – Agricultural Research Service - Plant Science, Research, St. Paul, MN 55108, USA; Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USADepartment of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USADepartment of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27606, USADepartment of Biological Systems Engineering, University of Wisconsin, Madison, WI 53706, USAUnited States Department of Agriculture – Agricultural Research Service - Plant Germplasm Introduction and Testing Research Unit, Prosser, WA 99350, USADepartment of Computer Science and Engineering, Mississippi State University, Starkville, MS, 39759, USAUnited States Department of Agriculture – Agricultural Research Service - Plant Science, Research, St. Paul, MN 55108, USA; Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA; Corresponding author.Traditional alfalfa stem phenotyping is labor-intensive and susceptible to bias from subjective ratings. Computer vision and machine learning present a promising solution for objectively assessing stem morphology. This study proposed an AI-driven image analysis to replace manual phenotyping methods with high efficiency and accuracy. We developed a novel pipeline that combines YOLOv8n with Otsu’s thresholding and K-means clustering to identify the medoids of internal and external polygons of the stem, thereby quantifying stem traits using pixel-based morphometric masks. The approach achieved an F1 score of 0.91 in detecting and classifying hollow or solid stems across plots and genotypes. Further analysis measured stem area and the proportions of stem tissue versus hollow regions, generating traits like hollowness score and percentage of hollowness. These stem-level metrics provide novel, objective, and quantitative phenotypic measurements, supporting ongoing chemical digestibility analyses and enabling real-time, image-based digestibility assessments through a field-deployable mobile application.http://www.sciencedirect.com/science/article/pii/S277237552500509XYOLOComputer visionAlfalfaImage analysisPixelStem hollowness |
| spellingShingle | Brandon J. Weihs Zhou Tang Somshubhra Roy Zezhong Tian Deborah Jo Heuschele Zhiwu Zhang Cranos Williams Zhou Zhang Garett Heineck Swayamjit Saha Zhanyou Xu No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level Smart Agricultural Technology YOLO Computer vision Alfalfa Image analysis Pixel Stem hollowness |
| title | No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level |
| title_full | No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level |
| title_fullStr | No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level |
| title_full_unstemmed | No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level |
| title_short | No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level |
| title_sort | no more laborious stem counting ai powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level |
| topic | YOLO Computer vision Alfalfa Image analysis Pixel Stem hollowness |
| url | http://www.sciencedirect.com/science/article/pii/S277237552500509X |
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