Deep Learning-based Autonomous Downy Mildew Detection and Severity Estimation in Vineyards

Liu, Ertai; Gold, Kaitlin M.; Combs, D.; Cadle‐Davidson, Lance; Jiang, Yu

doi:10.13031/aim.202100486

Cited by 5 publications

(1 citation statement)

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“…From these studies, various attention mechanisms (Niu et al, 2021) and multiscale feature fusion were the two important components contributing to the high model accuracy and generalizability, which should be retained in the future. To the best of our knowledge, our previous study was the only one to examine the use of deep semantic segmentation models for grape DM evaluation in the vineyard (Liu et al, 2021). While the hierarchical multi-scale attention for semantic segmentation (HMASS) model (Tao et al, 2020) was used and evaluated, the performance was obtained only on small training and validation datasets without a separate testing dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep semantic segmentation for the quantification of grape foliar diseases in the vineyard

et al. 2022

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Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs.

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Section: Introductionmentioning

confidence: 99%