Diago Maria P, Sanz-Garcia Andres, Millan Borja, Blasco Jose, Tardaguila Javier
Instituto de Ciencias de la Vid y del Vino, (University of La Rioja, CSIC, Gobierno de La Rioja), 26006, Logroño, La Rioja, Spain.
J Sci Food Agric. 2014 Aug;94(10):1981-7. doi: 10.1002/jsfa.6512. Epub 2014 Jan 7.
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.
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.
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.
花朵、开花和坐果是葡萄产量的关键决定因素。目前，用于估算坐果所需的每个花序花朵数量的实用方法既耗时又费力。这项工作旨在开发一种简单、廉价、快速、准确且稳健的机器视觉方法，应用于在田间条件下拍摄的RGB图像，以自动估算每个花序的花朵数量。
在开花前，使用袖珍RGB相机在葡萄园采集了90张酿酒葡萄品种丹魄、格拉西亚诺和佳丽酿单个花序的图像，并用于开发和测试“花朵计数”算法。在实际和自动估算花序花朵数量之间观察到了很强且显著的关系，三个品种的决定系数R²均高于80%，所有品种的精度均超过90%。
所开发的算法证明，在不受控制的户外条件下，用袖珍相机拍摄的数字图像分析能够自动对葡萄开花早期每个花序的花朵数量提供有用的估算。
