Assessment of flower number per inflorescence in grapevine by image analysis under field conditions.

BACKGROUND: Flowers, flowering and fruit set are key determinants of grapevine yield. Currently, practical methods to assess the flower number per inflorescence, necessary for fruit set estimation, are time and labour demanding. This work aims at developing a simple, cheap, fast, accurate and robust machine vision methodology to be applied to RGB images taken under field conditions, to estimate the number of flowers per inflorescence automatically.
RESULTS: Ninety images of individual inflorescences of Vitis vinifera L. cultivars Tempranillo, Graciano and Carignan were acquired in the vineyard with a pocket RGB camera prior to flowering, and used to develop and test the 'flower counting' algorithm. Strong and significant relationships, with R(2) above 80% for the three cultivars were observed between actual and automated estimation of inflorescence flower numbers, with a precision exceeding 90% for all cultivars.
CONCLUSION: The developed algorithm proved that the analysis of digital images captured by pocket cameras under uncontrolled outdoors conditions was able to automatically provide a useful estimation of the number of flowers per inflorescence of grapevines at early stages of flowering.