A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard

作者：Salvatore Filippo Di Gennaro, Piero Toscano, Paolo Cinat, Andrea Berton, Alessandro Matese · 发表于：Frontiers in Plant Science · 年份：2019 · DOI：10.3389/fpls.2019.00559 · 被引用次数：87 · 研究领域：Horticultural and Viticultural Research、Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics

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, w...