ABSTRACT
Infection of grapevines by fungal pathogens causing grapevine trunk diseases (GTDs) primarily arises from annual pruning wounds made during the dormant season. While various studies have showcased the efficacy of products in shielding pruning wounds against GTDs infections, most of these investigations hinge on artificial pathogen inoculations, which may not faithfully mirror real field conditions. This study aimed to evaluate and compare the efficacy of various liquid formulation fungicides (pyraclostrobin + boscalid) and paste treatments, as well as biological control agents (BCA: Trichoderma atroviride SC1, T. atroviride I-1237, and T. asperellum ICC012 + T. gamsii ICC080), for their potential to prevent natural infection of grapevine pruning wounds by trunk disease fungi in two field trials located in Samaniego (Northern Spain) and Madiran (Southern France) over three growing seasons. Wound treatments were applied immediately after pruning in February. One year after pruning, canes were harvested from vines and brought to the laboratory for assessment of Trichoderma spp. and fungal trunk pathogens. More than 1,200 fungal isolates associated with five GTDs (esca, Botryophaeria, Diaporthe and Eutypa diebacks, and Cytospora canker) were collected from the two vineyards each growing season. Our findings reveal that none of the products under investigation exhibited complete effectiveness against all the GTDs. The efficacy of these products was particularly influenced by the specific year of study. A notable exception was observed with the biocontrol agent T. atroviride I-1237, which consistently demonstrated effectiveness against Botryosphaeria dieback infections throughout each year of the study, irrespective of the location. The remaining products exhibited efficacy in specific years or locations against particular diseases, with the physical barrier (paste) showing the least overall effectiveness. The recovery rates of Trichoderma spp. in treated plants were highly variable, ranging from 17% to 100%, with both strains of T. atroviride yielding the highest isolation rates. This study underscores the importance of customizing treatments for specific diseases, taking into account the influence of environmental factors for BCA applications.
ABSTRACT
Yield prediction is crucial for the management of harvest and scheduling wine production operations. Traditional yield prediction methods rely on manual sampling and are time-consuming, making it difficult to handle the intrinsic spatial variability of vineyards. There have been significant advances in automatic yield estimation in vineyards from on-ground imagery, but terrestrial platforms have some limitations since they can cause soil compaction and have problems on sloping and ploughed land. The analysis of photogrammetric point clouds generated with unmanned aerial vehicles (UAV) imagery has shown its potential in the characterization of woody crops, and the point color analysis has been used for the detection of flowers in almond trees. For these reasons, the main objective of this work was to develop an unsupervised and automated workflow for detection of grape clusters in red grapevine varieties using UAV photogrammetric point clouds and color indices. As leaf occlusion is recognized as a major challenge in fruit detection, the influence of partial leaf removal in the accuracy of the workflow was assessed. UAV flights were performed over two commercial vineyards with different grape varieties in 2019 and 2020, and the photogrammetric point clouds generated from these flights were analyzed using an automatic and unsupervised algorithm developed using free software. The proposed methodology achieved R2 values higher than 0.75 between the harvest weight and the projected area of the points classified as grapes in vines when partial two-sided removal treatment, and an R2 of 0.82 was achieved in one of the datasets for vines with untouched full canopy. The accuracy achieved in grape detection opens the door to yield prediction in red grape vineyards. This would allow the creation of yield estimation maps that will ease the implementation of precision viticulture practices. To the authors' knowledge, this is the first time that UAV photogrammetric point clouds have been used for grape clusters detection.
