Hyper Spectral Imaging and Optimized Neural Networks for Early Detection of Grapevine Viral Disease
Bibliographic record
Abstract
The early detection of viral infections in grapevines is crucial to implement timely countermeasures and prevent the spread of disease across vineyards.This study leverages remote sensing via hyper spectral imaging to non-invasively identify and quantify infections caused by the recently discovered grapevine vein-clearing virus (GVCV), primarily during the initial asymptomatic phase.Post-calibration and preprocessing of hyper spectral images, only pixels associated with grapevines were retained.To discern between reflectance spectra profiles of healthy and GVCV-infected vines, an advanced statistical technique was employed.Subsequent to data preprocessing, an artificial hummingbird optimization technique was utilized for feature extraction, ensuring the selection of the most relevant features for enhancing the overall model classification.Furthermore, a non-invasive method was adopted to estimate the total chlorophyll (Chl) content of grape leaves.The study found a correlation between Chl concentration and the red-edge chlorophyll index, with reflectance measurements in the near-infrared (755-765 nm) and red-edge (710-720 nm) spectral ranges.For both pixel-wise and image-wise classification of disease severity, a hybrid of ZfNet+VGG19 was deployed.The proposed method, termed the Artificial Humming Bird Optimized ZfNet+VGG19 neural network (AHB_ZfNet+VGG19), demonstrated a considerable acceleration and an increase in accuracy, primarily attributed to the incorporation of prior training and model deepening.When contrasted with established methodologies, the proposed approach achieved a superior performance with an accuracy of 98.27%, precision of 97.67%, recall of 97.41% and F1-score of 97.74% for the Salinas dataset, and an accuracy of 98.45%, precision of 97.1%, recall of 97.41%, and F1-score of 97.6% for the Indian pine dataset.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".