Toward Estimating the Crop Coefficient of Vineyards Using a Smartphone Camera

Abstract

Abstract Background and goals Measuring evapotranspiration (ETc) in vineyards is important to optimize vineyard irrigation and water management practices. Previous work demonstrated a strong correlation between the amount of shaded area under the vine at high noon and crop coefficient. This parameter can be measured with a photovoltaic sensor (Paso Panel) or by hand using grid paper. We aimed to develop a low-cost and easy-to-use smartphone-based alternative to measure shaded area under a vine. Methods and key findings Videos of the ground under a row of vines were recorded with a smartphone camera on a sunny day in the presence of resident vegetation which consisted of grasses and weeds. A novel computer vision-based algorithm using a segmentation machine learning model and structure-from-motion was developed to estimate the amount of shaded area present. Measurements were collected using a Paso Panel at the same time for comparison. Other Paso Panel measurements were collected to measure the relationship between electrical current and shaded area. Linear regression of this CV-based method to Paso Panel readings yields R2 = 0.68. Conclusions and significance A new model for relating Paso Panel current readings to shaded area was derived empirically. Adoption of the CV-based crop coefficient estimation method could improve spatial resolution of ETc estimates, potentially aiding adoption of variable rate irrigation.

Plant phenotyping relevance

ブドウ樹下の遮光面積をスマートフォン映像とコンピュータビジョンで推定し、作物係数・蒸発散量推定に利用する手法を開発・比較検証しており、植物状態の取得手法が中心である。

Code and data availability

The paper's phenotype measurements (smartphone videos of shaded area under vines, Paso Panel readings) and analysis code are not publicly deposited. The authors state the underlying data are available only on request from the corresponding author, and the software is described as not yet publicly available. Generic SfM

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