High-Throughput Imaging System for Early Detection of Root Rot Disease in Field Peas (Pisum sativum L.)
Bibliographic record
Abstract
Aphanomyces root rot (ARR) and Fusarium root rot (FRR), caused by Aphanomyces euteiches and Fusarium avenaceum, are major soil-borne diseases limiting field pea productivity and breeding progress. This study presents a high-throughput hyperspectral imaging (HSI) system combined with machine learning (ML) to enable early, non-destructive detection of root rot in controlled greenhouse trials. Sixteen pea genotypes were evaluated across three experiments in 2024, with shoot and root reflectance data acquired using a HSI sensor. Visual disease ratings were categorized into three severity levels of low, moderate, and high for model development. ML algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Artificial Neural Network (ANN), were trained to predict disease severity. PLSR achieved the highest predictive accuracy, especially when using root spectra, which showed best performance in detecting high disease severity. Key wavelengths in the red-edge (680–750 nm) and near-infrared (750–1000 nm) regions were identified as critical for early disease inference. Preliminary relationships linking shoot and root spectra were also explored for field applicability. This integrated HSI-ML approach offers a scalable, rapid phenotyping tool for precision breeding, supporting the development of disease-resistant pea cultivars and advancing sustainable crop protection strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".