Abstract
Yield prediction is a key factor to optimize vineyard management and achieve the desired grape quality. Classical yield estimation methods, which consist of manual sampling within the field on a limited number of plants before harvest, are time-consuming and frequently insufficient to obtain representative yield data. Non-invasive machine vision methods are therefore being investigated to assess and implement a rapid grape yield estimate tool. This study aimed at an automated estimation of yield in terms of cluster number and size from high resolution RGB images (20 MP) taken with a low-cost UAV platform in representative zones of the vigor variability within an experimental vineyard. The flight campaigns were conducted in different light conditions and canopy cover levels for 2017 and 2018 crop seasons. An unsupervised recognition algorithm was applied to derive cluster number and size, which was used for estimating yield per vine. The results related to the number of clusters detected in different conditions, and the weight estimation for each vigor zone are presented. The segmentation results in cluster detection showed a performance of over 85% in partially leaf removal and full ripe condition, and allowed grapevine yield to be estimated with more than 84% of accuracy several weeks before harvest. The application of innovative technologies in field-phenotyping such as UAV, high-resolution cameras and visual computing algorithms enabled a new methodology to assess yield, which can save time and provide an accurate estimate compared to the manual method.
Highlights
Grapes are one of the most widely grown fruit crops in the world: vineyards cover a total area of 7.5 million hectares and produce a total yield of 75.8 million metric tons, of which 36% are fresh grapes, 8% raisins and 48% wine grapes (International Organisation of Vine and Wine [OIV], 2017).Monitoring and grading in-field grape ripeness and health status is extremely important for valuable production, for both the table grape and premium wine markets (Bindon et al, 2014; Ivorra et al, 2015; Portales and Ribes-Gomez, 2015; Pothen and Nuske, 2016)
Calculation of the NDVI values allowed the identification of two representative zones of the vigor heterogeneity within the
The application of innovative technologies in field phenotyping such as UAV, digital image analysis tools and image interpretation techniques promises a methodology for yield and quality traits estimation in a vineyard in order to rapidly monitor representative zones in a large acreage, improve the quality of recording and minimize error variation between samples
Summary
Grapes are one of the most widely grown fruit crops in the world: vineyards cover a total area of 7.5 million hectares and produce a total yield of 75.8 million metric tons, of which 36% are fresh grapes, 8% raisins and 48% wine grapes (International Organisation of Vine and Wine [OIV], 2017).Monitoring and grading in-field grape ripeness and health status is extremely important for valuable production, for both the table grape and premium wine markets (Bindon et al, 2014; Ivorra et al, 2015; Portales and Ribes-Gomez, 2015; Pothen and Nuske, 2016). As for other crops, the yield monitoring in terms of cluster number and size is key information in viticulture (Fanizza et al, 2005; Cabezas et al, 2006; Costantini et al, 2008). UAV Approach for Yield Estimation proposed by the International Organisation of Vine and Wine [OIV] (2007), which may involve problems such as low efficiency in terms of time and sampling representativeness (Pothen and Nuske, 2016). Specific user-coded computer vision applications of particle size may require advanced programming using a proprietary programming language environment such as Visual C or MATLAB with specialized image processing toolboxes (Igathinathane et al, 2008). This software provides many image analysis tools, including algorithms based on threshold detection value to generate the binary image used for segmentation. Otsu’s threshold is one of the most widely used threshold techniques for the vegetation segmentation process (Ling and Ruzhitsky, 1996; Shrestha et al, 2004). Gebhardt et al (2006) converted the RGB images into grayscale generating local homogeneity images to detect a homogeneity threshold
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