Background: Grapevine flower number per inflorescence provides valuable information that can be used for assessing yield. Considerable research has been conducted at developing a technological tool, based on image analysis and predictive modelling. However, the behaviour of variety-independent predictive models and yield prediction capabilities on a wide set of varieties has never been evaluated.
Results: Inflorescence images from 11 grapevine Vitis vinifera L. varieties were acquired under field conditions. The flower number per inflorescence and the flower number visible in the images were calculated manually, and automatically using an image analysis algorithm. These datasets were used to calibrate and evaluate the behaviour of two linear (single-variable and multivariable) and a nonlinear variety-independent model. As a result, the integrated tool composed of the image analysis algorithm and the nonlinear approach showed the highest performance and robustness (RPD = 8.32, RMSE = 37.1). The yield estimation capabilities of the flower number in conjunction with fruit set rate (R2 = 0.79) and average berry weight (R2 = 0.91) were also tested.
Conclusion: This study proves the accuracy of flower number per inflorescence estimation using an image analysis algorithm and a nonlinear model that is generally applicable to different grapevine varieties. This provides a fast, non-invasive and reliable tool for estimation of yield at harvest. © 2016 Society of Chemical Industry.
Keywords: computer vision; flowering; fruit set rate; multi-variety linear models; non-linear models; yield prediction.
© 2016 Society of Chemical Industry.