Machine vision based grain handling system
Bibliographic record
Abstract
A line scan and an area scan cameras were used to acquire images offive grain types namelybarley, Canada Westem AmberDurum (CWAD) wheat, Canada Westem Red Spring (CWRS) wheat, oats, and rye from across the various growing regions in the Westem Canadian prairies.Images rvere acquired by placing individual kemels in a non-touching manner in the camera's field of view and by packing them as a bulk sample in a petri dish.Software was developed using C++ on the windows platform to analyze images and extract 51 morphological, 123 color, and 56 textural features.Grain identif,rcation was tried using images of individual and bulk kemels.Classification of individual kemels was done using morphological, color, textural, and all the features combined together, whereas, for bulk samples, classification was tried using color, textural, and both the features combined.The top 20 features from each of morphological, color, and textural sets were selected and classification was carried outusing the combined 60.Using the top 10 features ofeach set, classification was carried out using the combined 30 features.Three types ofclassifiers, namely back propagation network, non- parametric, and specialist probabilistic neural network, were used for classification purposes.An mean classification accuracy of 96,9,94.3,95,and95.5%owereobtained for individual kemel images when the top 20 morphological, color, and texfural features were used fo grain classification using the back propagation network @PN), non-parametric, specialist probabilistic neural network (SPNN), and modified SPNN ciassifiers, respectively; For bulk sample images, classification accuracies close to 100% were obtained using top 20 color and top 20 textural features.A dual conveyorbelt system was designed and fabricated to present the grain samples to the line scan camera in a non-touching maruler on a continuous basis.A vibratory hopper was used to feed the grain to the primary conveyor belt.Use ofthe vibratory hopper and differential belt speed resulted in over 93%o instances of single kemels.A comparison of seven size features extracted from images ofsingle gtain kemels obtained from area and line scan cameras revealed that there was no variability-in size features.
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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) | 0.001 | 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".