Classification of Density and Transparency of Needle Leaves Types Using AlexNet and VGG16 Architecture
Bibliographic record
Abstract
This article discusses the application of digital image technology and deep learning using Convolutional Neural Network (CNN) in Forest Health Monitoring (FHM).Forest health monitoring is a method for measuring forest health, one of the parameters used is crown density and transparency.Measurement of these parameters is still done manually using magic cards so it is less effective and efficient, so it is necessary to apply digital images, one of which is the CNN algorithm to help measure the density scale and crown transparency.CNN architectures namely AlexNet and VGG16 are used to train the tree image recognition model.This research uses a tree image dataset with four types of needles grouped into classes based on crown density and transparency.The results showed that both CNN architectures achieved a good level of accuracy in classifying coniferous tree species based on crown density and transparency.VGG16 notably achieves higher accuracy than AlexNet.The results of model evaluation via the confusion matrix also provide insight into the model's performance in recognizing crown density and transparency classes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".