Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
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2024
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| _version_ | 1866915222408658944 |
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| author | Paudel, Achyut Brown, Jostan Upadhyaya, Priyanka Asad, Atif Bilal Kshetri, Safal Davidson, Joseph R. Grimm, Cindy Thompson, Ashley Sallato, Bernardita Whiting, Matthew D. Karkee, Manoj |
| author_facet | Paudel, Achyut Brown, Jostan Upadhyaya, Priyanka Asad, Atif Bilal Kshetri, Safal Davidson, Joseph R. Grimm, Cindy Thompson, Ashley Sallato, Bernardita Whiting, Matthew D. Karkee, Manoj |
| contents | Apple(\textit{Malus domestica} Borkh.) trees are deciduous, shedding leaves each year. This process is preceded by a gradual change in leaf color from green to yellow as chlorophyll is degraded prior to abscission. The initiation and rate of this color change are affected by many factors including leaf nitrogen (N) concentration. We predict that leaf color during this transition may be indicative of the nitrogen status of apple trees. This study assesses a machine vision-based system for quantifying the change in leaf color and its correlation with leaf nitrogen content. An image dataset was collected in color and 3D over five weeks in the fall of 2021 and 2023 at a commercial orchard using a ground vehicle-based stereovision sensor. Trees in the foreground were segmented from the point cloud using color and depth thresholding methods. Then, to estimate the proportion of yellow leaves per canopy, the color information of the segmented canopy area was quantified using a custom-defined metric, \textit{yellowness index} (a normalized ratio of yellow to green foliage in the tree) that varied from -1 to +1 (-1 being completely green and +1 being completely yellow). Both K-means-based methods and gradient boosting methods were used to estimate the \textit{yellowness index}. The gradient boosting based method proposed in this study was better than the K-means-based method (both in terms of computational time and accuracy), achieving an $R^2$ of 0.72 in estimating the \textit{yellowness index}. The metric was able to capture the gradual color transition from green to yellow over the study duration. Trees with lower leaf nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen.
Keywords: Fruit Tree Nitrogen Management, Machine Vision, Point Cloud Segmentation, Precision Nitrogen Management |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14653 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration Paudel, Achyut Brown, Jostan Upadhyaya, Priyanka Asad, Atif Bilal Kshetri, Safal Davidson, Joseph R. Grimm, Cindy Thompson, Ashley Sallato, Bernardita Whiting, Matthew D. Karkee, Manoj Computer Vision and Pattern Recognition Machine Learning Apple(\textit{Malus domestica} Borkh.) trees are deciduous, shedding leaves each year. This process is preceded by a gradual change in leaf color from green to yellow as chlorophyll is degraded prior to abscission. The initiation and rate of this color change are affected by many factors including leaf nitrogen (N) concentration. We predict that leaf color during this transition may be indicative of the nitrogen status of apple trees. This study assesses a machine vision-based system for quantifying the change in leaf color and its correlation with leaf nitrogen content. An image dataset was collected in color and 3D over five weeks in the fall of 2021 and 2023 at a commercial orchard using a ground vehicle-based stereovision sensor. Trees in the foreground were segmented from the point cloud using color and depth thresholding methods. Then, to estimate the proportion of yellow leaves per canopy, the color information of the segmented canopy area was quantified using a custom-defined metric, \textit{yellowness index} (a normalized ratio of yellow to green foliage in the tree) that varied from -1 to +1 (-1 being completely green and +1 being completely yellow). Both K-means-based methods and gradient boosting methods were used to estimate the \textit{yellowness index}. The gradient boosting based method proposed in this study was better than the K-means-based method (both in terms of computational time and accuracy), achieving an $R^2$ of 0.72 in estimating the \textit{yellowness index}. The metric was able to capture the gradual color transition from green to yellow over the study duration. Trees with lower leaf nitrogen showed the color transition to yellow earlier than the trees with higher nitrogen. Keywords: Fruit Tree Nitrogen Management, Machine Vision, Point Cloud Segmentation, Precision Nitrogen Management |
| title | Machine Vision-Based Assessment of Fall Color Changes and its Relationship with Leaf Nitrogen Concentration |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.14653 |