Predicting vineyard canopy coverage using drone pictures

Yufeng Zheng; Girish Panicker; Diane E. Stott

doi:10.1117/12.2585806

12 April 2021 Predicting vineyard canopy coverage using drone pictures

Yufeng Zheng, Girish Panicker, Diane E. Stott

Proceedings Volume 11747, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI; 1174706 (2021) https://doi.org/10.1117/12.2585806
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Yufeng Zheng, Girish Panicker, and Diane E. Stott "Predicting vineyard canopy coverage using drone pictures", Proc. SPIE 11747, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI, 1174706 (12 April 2021); https://doi.org/10.1117/12.2585806

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