Hemp Waste Classification Using Convolutional Neural Networks
Bibliographic record
Abstract
Canada’s cannabis industry generates a large amount of organic waste. This waste material has the potential to be reutilized for a variety of purposes, creating economic value. It has several uses in the fields of construction, healthcare, food and energy production, textile and paper manufacturing, and the automobile industry. Grown year round, hemp is a plant material that yields a great number of seeds, fibers, and medicines. Hemp is divided into many groups according to its energy content, nutritional value, physical characteristics, and economic benefits. Making a machine learning model to classify hemp is therefore quite helpful in differentiating between them. A convolutional neural networks (CNN) based model is introduced to perform the classification of hemp waste by considering the morphological features (color and texture). Using an industrial camera, three hundred and twenty-two photos were gathered for different types of hemp waste as part of the data collecting process. Cross-validation and data augmentation were utilized to enhance this dataset. The performance of the model was assessed using accuracy as well as additional machine learning testing parameters. The model's testing accuracy score was approximately 96%. The fact that the proposed approach can be turned into a smartphone app makes it appear highly promising.
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 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) | 0.004 | 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".