Using a Camera System for the In-Situ Assessment of Cordon Dieback due to Grapevine Trunk Diseases

Abstract

Background and Aims. The assessment of grapevine trunk disease symptoms is a labour-intensive process that requires experience and is prone to bias. Methods that support the easy and accurate monitoring of trunk diseases will aid management decisions. Methods and Results. An algorithm was developed for the assessment of dieback symptoms due to trunk disease which is applied on a smartphone mounted on a vehicle driven through the vineyard. Vine images and corresponding expert ground truth assessments (of over 13,000 vines) were collected and correlated over two seasons in Shiraz vineyards in the Clare Valley, Barossa, and McLaren Vale, South Australia. This dataset was used to train and verify YOLOv5 models to estimate the percentage dieback of cordons due to trunk diseases. The performance of the models was evaluated on the metrics of highest confidence, highest dieback score, and average dieback score across multiple detections. Eighty-four percent of vines in a test set derived from an unseen vineyard were assigned a score by the model within 10% of the score given by experts in the vineyard. Conclusions. The computer vision algorithms were implemented within the phone, allowing real-time assessment and row-level mapping with nothing more than a high-end mobile phone. Significance of the Study. The algorithms form the basis of a system that will allow growers to scan their vineyards easily and regularly to monitor dieback due to grapevine trunk disease and will facilitate corrective interventions.