A Machine Learning Framework for Classifying Thermal Stress in Bean Plants Using Hyperspectral Data
Why this work is in the frame
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Bibliographic record
Abstract
Rising global temperatures pose a significant threat to agricultural productivity, making the early detection of plant stress crucial for minimizing crop losses. While hyperspectral remote sensing is a powerful tool for monitoring plant health, the precise spectral regions and most effective machine learning models for detecting thermal stress remain an open research question. This study presents a robust framework that utilizes eight state-of-the-art machine learning algorithms to classify the reflectance response of thermal-induced stress in two cultivars of bean plants. Our controlled experiment measured hyperspectral data across two growth stages and three stress conditions (pre-stress, during stress, and post-stress) using a spectroradiometer. The results demonstrate the high performance of several algorithms, with the Artificial Neural Network (ANN) achieving an impressive 99.4% overall accuracy. A key contribution of this work is the identification of the most contributory spectral ranges for thermal stress discrimination: the green region (530–570 nm) and the red-edge region (700–710 nm). This framework is a feasible and effective tool for modelling the hyperspectral response of thermal-stressed bean plants and provides critical guidance for future research on stress-specific spectral indices.
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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 it