Dr. Diane E. Stott Profile

Dr. Diane E. Stott

at Purdue Univ

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Publications (2)

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Proceedings Article | 1 June 2022 Presentation + Paper

Vineyard LAI and canopy coverage estimation with convolutional neural network models and drone pictures

Yufeng Zheng, Erol Sarigul, Girish Panicker, Diane Stott

Proceedings Volume 12120, 1212006 (2022) https://doi.org/10.1117/12.2620100

KEYWORDS: Transformers, Convolutional neural networks, Neural networks

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We developed convolutional neural network (CNN) models using drone pictures to estimate vineyard leaf area index (LAI) and percent canopy cover. These parameters are traditionally measured using hand-held devices (e.g., AccuPAR for LAI and Ceptometer for percent canopy cover) and calculated manually, which is labor intensive and hard to apply to large-scale areas. We collected airborne images or videos by flying a low-altitude drone with a built-in digital camera over a large-scale vineyard. The airborne images convey all necessary information for developing CNN models. To date, we have collected data from the same vineyard over a couple of years. The ground truth values were manually measured using AccuPAR and Ceptometer at the same time of airborne imaging. Specifically, we trained five CNN models to estimate percent canopy cover and leaf area index (LAI). The estimated results over a large vineyard will help guide planting intercrops or cover crops to prevent soil erosion, calculating the correct amounts of expected residue in fall, and foliar sprays of pesticides and fungicides, and characterization of vegetation-atmosphere interactions.

Proceedings Article | 12 April 2021 Presentation + Paper

Predicting vineyard canopy coverage using drone pictures

Yufeng Zheng, Girish Panicker, Diane Stott

Proceedings Volume 11747, 1174706 (2021) https://doi.org/10.1117/12.2585806

KEYWORDS: Spherical lenses, Photography, Neural networks, Image analysis, Digital cameras

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The common techniques used to estimate tree canopy coverage are line-intercept, spherical densiometer, moosehorn or hemispherical photography, all which demand intensive manual operations both in data collection (typically underneath the trees) and in post-processing the results, calculations and reports. These labor-intensive techniques result in high costs and are difficult to apply to large scale areas. We propose acquiring airborne images by flying a low-altitude drone with a built-in digital camera over a large-scale vineyard. The airborne images convey all necessary information, and the image analysis techniques plus deep learning neural network can create a set of regression models for the anticipated calculations. Specifically, we can predict leaf area index (LAI) and percent canopy cover, which will provide guidance for planting intercrops or cover crops to prevent soil erosion and improve soil health, determine the photosynthetic and transpirational surface of plant canopies, ecophysiology, water balance modeling, in calculating the correct amounts of foliar sprays of pesticides or fungicides, and characterization of vegetation-atmosphere interactions.

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