vitisBerry: An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis

Abstract

In agriculture, crop monitoring and plant phenotyping are mainly manually measured. However, this practice gathers phenotyping information at a lower rate than genotyping evolves, thus producing bottleneck. This paper presents vitisBerry, a smartphone application for assessing in the vineyard, using computer vision, the berry number in clusters at phenological stages between berry-set and cluster-closure. The implemented image analysis algorithm is an evolution of a previous development, providing 1.63% and 7.57% of Recall and Precision improvement, respectively. The application was evaluated using two devices, taking and analysing 144 images from 12 different grapevine varieties. The Recall and Precision results ranged between 0.8762 and 0.9082 and 0.9392–0.9508, depending on the device. The average computational time required to analyse the 144 images varied from 3.14 to 8.40 s. According to these results, vitisBerry constitutes a tool for viticulturists to acquire phenotyping information from their vineyards in an easy and practical way.