Interpretation of Hyperspectral Images Using Integrated Gradients to Detect Bruising in Lemons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lemons are a popular citrus fruit known for their medicinal and nutritional properties. However, fresh lemons are vulnerable to mechanical damage during transportation, with bruising being a common issue. Bruising reduces the fruit’s shelf life and increases the risk of bacterial and fungal contamination, leading to economic losses. Furthermore, discoloration typically occurs after 24 h, so it is crucial to detect bruised fruits promptly. This paper proposes a novel method for detecting bruising in lemons using hyperspectral imaging and integrated gradients. A dataset of hyperspectral images was captured in the wavelength range of 400–1100 nm for lemons that were sound and artificially bruised (8 and 16 h after bruising), with three distinct classes of images corresponding to these conditions. The dataset was divided into three subsets i.e., training (70%), validation (20%), and testing (10%). Spatial–spectral data were analyzed using three 3D-convolutional neural networks: ResNetV2, PreActResNet, and MobileNetV2 with parameter sizes of 242, 176, and 9, respectively. ResNetV2 achieved the highest classification accuracy of 92.85%, followed by PreActResNet at 85.71% and MobileNetV2 at 83.33%. Our results demonstrate that the proposed method effectively detects bruising in lemons by analyzing darker pixels in the images, subsequently confirming the presence of bruised areas through their spatial distribution and accumulation. Overall, this study highlights the potential of hyperspectral imaging and integrated gradients for detecting bruised fruits, which could help reduce food waste and economic losses.
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.
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 | 0.002 |
| 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