Lightweight AI Spots Grapevine Virus From Drone Hyperspectral Images

Grapevine red blotch virus has become one of the most economically damaging pathogens in North American viticulture, quietly draining value from vineyards by blocking sugar transport in berries, delaying ripening, and dulling the color and fruit character of wine. A new study published in Smart Agricultural Technology reports that a compact artificial intelligence model, trained on drone-collected hyperspectral imagery, can identify infected vines across entire commercial vineyards with an accuracy of roughly 75 percent and a recall of 87 percent, offering vineyard managers a scalable alternative to slow visual scouting and costly laboratory testing.

The research team, led by Alireza Sanaeifar of California State University together with Eve Laroche-Pinel, virologist Marc Fuchs, and Luca Brillante, worked across four commercial vineyards in Napa Valley, California, growing Cabernet Sauvignon and Cabernet Franc. Over two growing seasons, they geolocated 714 vines with satellite navigation, collected petiole samples from each, and confirmed infection status using endpoint multiplex PCR. The resulting dataset contained 399 infected and 315 non-infected vines, an approximately balanced distribution that strengthens the reliability of the classification results.

To capture the spectral fingerprints of infection, the team flew a DJI Matrice 600 Pro drone carrying a Senop HSC-2 snapshot hyperspectral camera with 29 spectral bands spanning 520 to 820 nanometers. Flights at 30 meters altitude and 5 meters per second produced imagery with a ground resolution of 2 by 2 centimeters per pixel. Because the camera records its bands sequentially, the researchers orthorectified each band individually and verified that residual misregistration stayed below roughly 5 centimeters. Reflectance was calibrated using a white reference panel placed in the field, and orthomosaics built in Agisoft Metashape allowed each PCR-tested vine to be matched precisely to its canopy pixels.

The spectral analysis revealed how subtle the disease signal really is. Infected and healthy vines showed nearly identical overall reflectance curves, with statistically significant differences concentrated in the green region between 540 and 580 nanometers, where healthy vines reflected more light, likely because infection degrades chlorophyll and alters pigment composition. Infected vines also showed localized reflectance increases around 740 to 760 nanometers, hinting at changes in internal leaf structure. Notably, the widely used NDVI vegetation index failed to separate the classes, while the Green NDVI and the Anthocyanin Reflectance Index both discriminated strongly, the latter consistent with the anthocyanin accumulation that gives red blotch disease its name.

Conventional vegetation indices, however, proved too blunt for reliable diagnosis, which is where the deep learning model enters. The researchers designed CompactSpectralViT, a lightweight patch-based vision transformer tailored to hyperspectral data. Each vine’s variable-sized canopy cube was standardized to 64 by 64 pixels across 29 bands through a three-stage pipeline of proportional resizing, adaptive center cropping, and final adjustment. The cube was then divided into 64 patches of 8 by 8 pixels, each carrying all 29 spectral values, and passed through a two-stage embedding that compressed each patch to just 48 dimensions. Four transformer encoder blocks with three attention heads each then let the model weigh relationships across the entire canopy simultaneously, capturing long-range spectral-spatial dependencies that convolutional networks process only locally.

Evaluated with 10-fold stratified cross-validation, CompactSpectralViT achieved a mean accuracy of 75.3 percent, precision of 74.5 percent, recall of 87.2 percent, an F1-score of 79.8 percent, and a ROC-AUC of 0.744. The high recall is operationally significant: in disease monitoring, a missed infected vine can seed further spread by the three-cornered alfalfa hopper, the virus’s insect vector, whereas a false alarm costs comparatively little. By tuning the classification threshold per fold, the model shifted its decision boundary toward sensitivity, accepting more false positives in exchange for catching nearly nine in ten infected vines.

The compact architecture also delivered a striking computational advantage. Benchmarking on an NVIDIA RTX 3090 showed that CompactSpectralViT required only 16.6 million multiply-accumulate operations per inference, roughly 7.6 times fewer than a convolutional baseline and a hybrid CNN-transformer comparator, and used just 138 megabytes of GPU memory versus 376 and 440 megabytes for the alternatives. It also outperformed both deep learning baselines in accuracy, recall, and ROC-AUC, suggesting that direct patch-level tokenization lets self-attention operate across the whole canopy from the first layer rather than relying on late-stage reasoning bolted onto convolutional features.

Interpretability analysis added a second layer of insight. SHAP-based feature importance and permutation importance, two independent attribution methods, agreed on nine of their ten most informative wavelengths. When the model was retrained on only the ten SHAP-selected bands, accuracy held at 75.6 percent, essentially matching the full 29-band system. The selected wavelengths clustered in three physiologically meaningful regions: the green band from 520 to 570 nanometers, tied to chlorophyll stress; the orange-red region from 610 to 620 nanometers, linked to anthocyanin accumulation; and the near-infrared from 780 to 820 nanometers, sensitive to leaf structure and water content. The authors caution that these findings guide sensor design but do not prove that a true multispectral camera would perform identically, since bandwidth and noise characteristics differ.

Against traditional machine learning on the same ten-wavelength input, the gap was decisive. A support vector machine reached only 62.9 percent accuracy and a random forest 65.1 percent, compared with 75.6 percent for the transformer, which also showed lower variability across folds. Part of this advantage reflects input representation, since the classical models received flattened feature vectors that discard spatial structure, but the result still underscores that self-attention over spectral-spatial patches extracts disease signatures that conventional classifiers miss. The accuracy itself is comparable to earlier ground-based hyperspectral studies of the virus, but the new work achieves it while imaging entire vineyard blocks by drone rather than one vine at a time, and across four sites and two cultivars.

The study’s authors are candid about limitations. Because sites and seasons were pooled across validation folds, transferability to unseen vineyards remains untested, and the infected class mixed symptomatic and asymptomatic vines, so the reported accuracy reflects detection under mixed symptom expression rather than purely asymptomatic diagnosis. Still, the work demonstrates that lightweight transformer architectures can perform credibly with limited labeled data under real field conditions, and it charts a practical path forward: automated vine segmentation, longitudinal monitoring of individual vines, integration with precision roguing programs, and eventually multi-disease frameworks that distinguish viral, fungal, and abiotic stresses from the air. For an industry where infection costs can reach tens of thousands of dollars per hectare over a vineyard’s lifetime, a drone and a compact neural network may soon become standard tools in the fight against an invisible pathogen.

Subject of Research: UAV hyperspectral imaging and a lightweight vision transformer for detecting grapevine red blotch virus in vineyards

Article Title: CompactSpectralViT: A lightweight vision transformer for grapevine red blotch virus detection from UAV hyperspectral imagery

Article References: Sanaeifar, A., Laroche-Pinel, E., Fuchs, M., & Brillante, L. (2026). CompactSpectralViT: A lightweight vision transformer for grapevine red blotch virus detection from UAV hyperspectral imagery. Smart Agricultural Technology, 15, Article 102599. https://doi.org/10.1016/j.atech.2026.102599

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102599

Keywords: grapevine red blotch virus, hyperspectral imaging, vision transformer, UAV remote sensing, precision viticulture, deep learning, plant disease detection, Napa Valley, PCR validation, feature importance, SHAP, multispectral sensors