Deep learning-based autonomous downy mildew detection and severity estimation in vineyards. Liu, E., Gold, K. M., Combs, D., Cadle-Davidson, L., & Jiang, Y. In 2021 ASABE Annual International Virtual Meeting, of ASABE Paper No. 2100486, pages 1, 2021. ASABE. ![Deep learning-based autonomous downy mildew detection and severity estimation in vineyards [link]](https://bibbase.org/img/filetypes/link.svg "link")
Link doi abstract bibtex 13 downloads Downy mildew (DM) poses a significant challenge for many high value crops such as grapes. DM can appear at nearly all stages of grapevine growth on clusters, canes, and leaves, and can result in total crop loss. Traditional DM assessment methods rely on in-field human observations or laboratory analysis of plant characteristics, which are laborious and costly for periodical tracking of the disease. The overall goal of this study was to develop an effective deep learning-based approach for the quantification of DM infection in vineyards. A custom ATV-based imaging system was used to collect georeferenced stereo images of grapevines in a vineyard, forming a dataset of 2072 image pairs. Human experts manually assessed the grapevines DM infection severity on the same day of data collection. A total of 58 images were selected and manually annotated to train and validate a segmentation model for DM detection in stereo image pairs. A Hierarchical Multi-Scale Attention Semantic Segmentation (HMASS) network was selected as the segmentation model. Based on the geolocation, the stereo pairs were segregated into each vine block. The trained DM detector was used to identify infection regions in all stereo pairs in a vine block, and all detections were projected onto the 3D space to remove duplicated detections. The ratio between area of leaves calculated by image color filtering and area of infections given by segmentation model was used to quantify the infection severity in each block. Experimental results showed that the trained HMASS detector could accurately identify DM infected regions. Infection severity rates calculated using the developed approach were highly correlated (r=0.96) with the human field assessment. The infection severity ranking of all 6 DM treatments in the vineyard provided by the developed approach were also identical to the human assessments. These results suggest that the developed approach can be used for rapid and accurate DM detection, which lays the foundation for the development of automated DM quantification and management systems.
@inproceedings{RN37,
author = {Liu, Ertai and Gold, Kaitlin M. and Combs, David and Cadle-Davidson, Lance and Jiang, Yu},
title = {Deep learning-based autonomous downy mildew detection and severity estimation in vineyards},
booktitle = {2021 ASABE Annual International Virtual Meeting},
series = {ASABE Paper No. 2100486},
publisher = {ASABE},
pages = {1},
abstract = {Downy mildew (DM) poses a significant challenge for many high value crops such as grapes. DM can appear at nearly all stages of grapevine growth on clusters, canes, and leaves, and can result in total crop loss. Traditional DM assessment methods rely on in-field human observations or laboratory analysis of plant characteristics, which are laborious and costly for periodical tracking of the disease. The overall goal of this study was to develop an effective deep learning-based approach for the quantification of DM infection in vineyards. A custom ATV-based imaging system was used to collect georeferenced stereo images of grapevines in a vineyard, forming a dataset of 2072 image pairs. Human experts manually assessed the grapevines DM infection severity on the same day of data collection. A total of 58 images were selected and manually annotated to train and validate a segmentation model for DM detection in stereo image pairs. A Hierarchical Multi-Scale Attention Semantic Segmentation (HMASS) network was selected as the segmentation model. Based on the geolocation, the stereo pairs were segregated into each vine block. The trained DM detector was used to identify infection regions in all stereo pairs in a vine block, and all detections were projected onto the 3D space to remove duplicated detections. The ratio between area of leaves calculated by image color filtering and area of infections given by segmentation model was used to quantify the infection severity in each block. Experimental results showed that the trained HMASS detector could accurately identify DM infected regions. Infection severity rates calculated using the developed approach were highly correlated (r=0.96) with the human field assessment. The infection severity ranking of all 6 DM treatments in the vineyard provided by the developed approach were also identical to the human assessments. These results suggest that the developed approach can be used for rapid and accurate DM detection, which lays the foundation for the development of automated DM quantification and management systems.},
keywords = {computer vision
plant disease
downy mildew
machine learning
proximal sensing
vineyard management.},
DOI = {https://doi.org/10.13031/aim.202100486},
url_Link = {https://elibrary.asabe.org/abstract.asp?aid=52414&t=5},
year = {2021},
type = {Conference Proceedings}
}
Downloads: 13
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DM can appear at nearly all stages of grapevine growth on clusters, canes, and leaves, and can result in total crop loss. Traditional DM assessment methods rely on in-field human observations or laboratory analysis of plant characteristics, which are laborious and costly for periodical tracking of the disease. The overall goal of this study was to develop an effective deep learning-based approach for the quantification of DM infection in vineyards. A custom ATV-based imaging system was used to collect georeferenced stereo images of grapevines in a vineyard, forming a dataset of 2072 image pairs. Human experts manually assessed the grapevines DM infection severity on the same day of data collection. A total of 58 images were selected and manually annotated to train and validate a segmentation model for DM detection in stereo image pairs. A Hierarchical Multi-Scale Attention Semantic Segmentation (HMASS) network was selected as the segmentation model. 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