Toward Estimating the Crop Coefficient of Vineyards Using a Smartphone Camera

<h3>Abstract</h3> <h3>Background and goals</h3> Measuring evapotranspiration (ET_c) 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. <h3>Methods and key findings</h3> 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. <h3>Conclusions and significance</h3> 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 ET_c estimates, potentially aiding adoption of variable rate irrigation.