Deep learning models can reliably detect the telltale leaf damage caused by grapevine leafhoppers under real field conditions, according to a new study published in Smart Agricultural Technology. The research, conducted by a team of Italian entomologists and agricultural engineers in Sardinia, establishes the first dedicated field dataset of hopperburn symptoms and a rigorous benchmark for evaluating object detection models in commercial vineyards. The findings arrive at a critical moment for viticulture, as European policies push for substantial pesticide reductions and growers urgently need new tools for early pest detection.
Grapevines across central and southern Europe face persistent pressure from two green leafhopper species, Jacobiasca lybica and Hebata vitis. These insects feed on phloem sap and produce a distinctive injury known as hopperburn, which begins as yellowing along leaf margins and can progress to reddening or yellowing between the veins, depending on whether the cultivar produces red or white grapes. Under heavy infestation, leaves may dry out entirely, reducing photosynthetic activity and ultimately compromising both yield and berry quality. Because the two leafhopper species generate visually indistinguishable symptoms, the researchers designed their system to detect leafhopper-induced damage in general rather than to identify the culprit species.
Diagnosing hopperburn in the field is notoriously difficult. The symptoms overlap with those caused by viral diseases such as grapevine leafroll-associated viruses, phytoplasma diseases like Flavescence doree, fungal pathogens, nutritional deficiencies, and herbicide drift. Current monitoring relies on yellow sticky traps for adults and laborious visual inspection of leaves for nymphs, but adult captures do not correlate with actual plant damage, so control decisions must be based on leaf infestation levels that are extremely time-consuming to assess across large areas. An automated system capable of flagging symptomatic leaves could transform this workflow.
The research team collected images over two growing seasons, from June to October 2021 and July to August 2022, across 23 commercial vineyards in Sardinia on 21 sampling dates. The vineyards encompassed 15 white and red wine cultivars differing in age, training system, and pest management strategy. Images were captured with commercial smartphones, such as an iPhone 12, at roughly 50 centimeters from symptomatic leaves under natural and deliberately varied light conditions, with backgrounds including healthy leaves, woody organs, and landscape elements. In total, 316 symptomatic images were selected in 2021 and 2,561 in 2022. Expert entomologists reviewed every image, and any showing ambiguous symptoms that might instead reflect potassium deficiency, viral infection, or other disorders were excluded to keep the ground truth clean.
Each image was manually annotated with bounding boxes around symptomatic leaves using the VGG Image Annotator software, producing four symptom classes: early and severe symptoms on white cultivars, and early and severe symptoms on red cultivars. To confirm which leafhopper species was present, the team deployed yellow sticky traps and identified adult males under a light microscope at 100x magnification using genitalia morphology. Across 115 microscope slides, every identifiable specimen proved to be J. lybica, and no males of H. vitis were recorded in the investigated vineyards.
Three modern object detection architectures were benchmarked: YOLOv5 as a widely adopted baseline, the newer YOLOv11, and RF-DETR, a transformer-based detector. All models were trained with identical datasets, annotation protocols, and training schedules on a Google Colab virtual machine equipped with an Nvidia Tesla P100 GPU. Data augmentation, including horizontal flipping, blurring, contrast enhancement via CLAHE, and HSV color adjustments, was applied to compensate for the limited dataset size. Performance was measured using standard object detection metrics, with the intersection over union threshold set at 50 percent, and macro mean Average Precision serving as the primary metric because it jointly evaluates localization and classification across all symptom classes.
The most striking result concerned the evaluation strategy itself. When images were randomly split into training, validation, and test sets, all three models performed superbly, with RF-DETR reaching a macro-mAP of 0.975, YOLOv5 0.944, and YOLOv11 0.899. But random splitting allows nearly identical images from the same vineyard, date, and cultivar to appear in both training and test data, inflating apparent accuracy. When the researchers instead partitioned the dataset so that no vineyard-cultivar combination was shared between training and test sets, performance dropped dramatically, with macro-mAP falling by 30 to 38 percentage points to between 0.517 and 0.678. This decline mirrors earlier findings in grape disease detection, where models trained on one cultivar achieved a true positive rate of 0.98 for Flavescence doree on Chardonnay but collapsed to 0.08 when tested on Ugni-Blanc.
Crucially, the analysis revealed that the residual errors under realistic partitioning stemmed mostly from misclassification among visually similar symptom classes rather than from failure to detect damage. When all hopperburn categories were treated as a single detection class, average precision exceeded 0.96 for all three models on the four-class partitioned dataset and remained above 0.95 on a three-class variant. Merging the two early symptom classes into a single category also boosted performance substantially, raising overall macro-mAP by 18 to 26 percentage points. Severe symptoms on red cultivars, with their pronounced red marginal and interveinal discoloration, were detected with high accuracy regardless of architecture or partitioning strategy, while the subtle white early class proved hardest to discriminate. Statistical comparisons using Fisher’s exact tests with Holm correction confirmed that dataset partitioning exerted a stronger influence on performance than the choice of detector architecture.
The practical implications are significant. Because misclassifications among symptom classes are far less consequential than missed detections or false alarms in an infestation-monitoring context, the study suggests that the operational value of these detectors as early warning systems may be underestimated by strict class-based metrics. The authors envision integration into mobile applications or autonomous ground platforms for real-time in-field monitoring and spatial mapping during routine vineyard operations. Such tools would fit a broader agricultural context in which mating disruption has reduced insecticide use against other key pests, the European Union’s Farm to Fork Strategy targets major pesticide cuts by 2030, and only four new synthetic active substances have been registered since 2011.
The study is not without limitations. All images were collected within a single viticultural region, and the team emphasizes that further validation under different environmental conditions and viticultural contexts is still required. Future work should expand the dataset to additional grape-growing regions with more balanced representation of cultivars, symptom severities, infestation-free vineyards, and visually similar biotic and abiotic disorders. Nevertheless, by pairing the first dedicated multi-temporal, multi-varietal field dataset of hopperburn with a rigorous, cultivar-independent evaluation framework, the research provides a foundation for objectively comparing future computer vision tools and marks a concrete step toward automated, sustainable pest management in the world’s vineyards.
Subject of Research: Deep learning detection of grapevine leafhopper hopperburn symptoms in vineyards
Article Title: Automatic detection of grapevine hopperburn symptoms using deep learning under field conditions
Article References: Cocco, A., Ghiani, L., El Moussaoui, S., Mannu, R., Sassu, A., Deidda, A., Santoro, F., Lentini, A., & Gambella, F. (2026). Automatic detection of grapevine hopperburn symptoms using deep learning under field conditions. Smart Agricultural Technology, 15, Article 102576. https://doi.org/10.1016/j.atech.2026.102576
Image Credits: AI Generated
DOI: 10.1016/j.atech.2026.102576
Keywords: deep learning, object detection, grapevine, leafhoppers, hopperburn, precision viticulture, YOLO, RF-DETR, pest monitoring, computer vision, Sardinia, sustainable agriculture
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Alan Morgan. (October 1, 2026). AI Learns to Spot Leafhopper Damage in Vineyards Under Real-World Conditions. Scienmag. https://scienmag.com/ai-learns-to-spot-leafhopper-damage-in-vineyards-under-real-world-conditions/
Alan Morgan. “AI Learns to Spot Leafhopper Damage in Vineyards Under Real-World Conditions.” Scienmag, 1 October 2026, https://scienmag.com/ai-learns-to-spot-leafhopper-damage-in-vineyards-under-real-world-conditions/. Accessed 1 October 2026.
Alan Morgan. “AI Learns to Spot Leafhopper Damage in Vineyards Under Real-World Conditions.” Scienmag. October 1, 2026. https://scienmag.com/ai-learns-to-spot-leafhopper-damage-in-vineyards-under-real-world-conditions/
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Tags: AI-based vineyard health monitoringcomputer visiondeep learningdeep learning for agricultural disease diagnosisearly pest detection in vineyardsfield dataset for pest symptom recognitiongrapevinegrapevine leaf damage identificationgrapevine pest managementhopperburnhopperburn symptom analysisimpact of leafhopper infestation on grape yieldLeafhopper damage detection in vineyardsleafhoppersobject detectionobject detection models in viticulturepest monitoringpesticide reduction in European viticultureprecision viticultureRF-DETRSardiniasustainable agriculturesustainable pest control strategiesYOLO
