Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis

New technologies in precision viticulture are increasingly being used to improve grape quality. One of the main challenges being faced by the scientific community in viticulture is early yield prediction. Within this framework, flowering as well as fruit set assessment is of special interest since these two physiological processes highly influence grapevine yield. In addition, an accurate fruit set evaluation can only be performed by means of flower counting. Herein a new methodology for segmenting inflorescence grapevine flowers in digital images is presented. This approach, based on mathematical morphology and pyramidal decomposition, constitutes an outstanding advance with respect to other previous approaches since it can be applied on images with uncontrolled background. The algorithm was tested on 40 images of 4 different Vitis vinifera L. varieties, and resulted in high performance. Specifically, values for Precision and Recall were 83.38% and 85.01%, respectively. Additionally, this paper also proposes a comprehensive study on models for estimating actual flower number per inflorescence. Results and conclusions that are developed in the literature and treated herewith are also clarified. Furthermore, the use of non-linear models as a promising alternative to previously-proposed linear models is likewise suggested in this study.