Near Real-Time Vineyard Downy Mildew Detection and Severity Estimation. Liu, E., Gold, K., Cadle-Davidson, L., Combs, D., & Jiang, Y. IEEE International Conference on Intelligent Robots and Systems, 2022-Octob:9187-9194, Institute of Electrical and Electronics Engineers Inc., 2022. ![Near Real-Time Vineyard Downy Mildew Detection and Severity Estimation [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex The global grape and wine industry has been considerably impacted by diseases such as downy mildew (DM). Agricultural robots have demonstrated great potential to accurately and rapidly map DM infection for precision applications. Although the robots can autonomously acquire high-resolution images in the vineyard, data processing is mostly performed offline because of network infrastructure and onboard computing power constraints, limiting the use of agricultural robots for field operations. To address this issue, we developed a semantic segmentation model based on the modified DeepLabv3 network for near real time DM segmentation in high resolution images. Compared with state-of-the-art real time semantic segmentation models, the developed one achieved the best efficiency-accuracy balance on the DM dataset using embedded computing devices that can be easily integrated with commercial robotic platforms. DM severity estimation pipeline based on the model also showed a comparable measurement accuracy and statistical power in differentiation of fungicide treatments as the one based on offline semantic segmentation models. This enables the use of robotic perception systems for field operations.
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title = {Near Real-Time Vineyard Downy Mildew Detection and Severity Estimation},
type = {article},
year = {2022},
pages = {9187-9194},
volume = {2022-Octob},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
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abstract = {The global grape and wine industry has been considerably impacted by diseases such as downy mildew (DM). Agricultural robots have demonstrated great potential to accurately and rapidly map DM infection for precision applications. Although the robots can autonomously acquire high-resolution images in the vineyard, data processing is mostly performed offline because of network infrastructure and onboard computing power constraints, limiting the use of agricultural robots for field operations. To address this issue, we developed a semantic segmentation model based on the modified DeepLabv3 network for near real time DM segmentation in high resolution images. Compared with state-of-the-art real time semantic segmentation models, the developed one achieved the best efficiency-accuracy balance on the DM dataset using embedded computing devices that can be easily integrated with commercial robotic platforms. DM severity estimation pipeline based on the model also showed a comparable measurement accuracy and statistical power in differentiation of fungicide treatments as the one based on offline semantic segmentation models. This enables the use of robotic perception systems for field operations.},
bibtype = {article},
author = {Liu, Ertai and Gold, Kaitlin and Cadle-Davidson, Lance and Combs, David and Jiang, Yu},
doi = {10.1109/IROS47612.2022.9981404},
journal = {IEEE International Conference on Intelligent Robots and Systems},
keywords = {liu2022nearrealtimevieyard}
}
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