A deep learning-based mobile application for tree species mapping in RGB images
Bibliographic record
Abstract
Tree species mapping is an important type of information demanded in different study fields. However, this task can be expensive and time-consuming, making it difficult to monitor extensive areas. Hence, automatic methods are required to optimize tree species mapping. Here, we propose a deep learning-based mobile application tool for tree species classification in high-spatial-resolution RGB images. Several deep learning architectures were evaluated, including mobile networks and traditional models. A total of 2,349 images were used, of which 1,174 images consisted of the Dipteryx alata species and 1,175 images of other local species. These images were manually annotated and randomly divided into training (70%), validation (20%), and testing (10%) subsets, considering the five-fold cross-validation. We evaluated the accuracy and speed (GPU and CPU) of all the implemented deep learning architectures. We found out that the traditional networks have the best performance in terms of F1 score; however, mobile networks are faster. Inception V3 model achieved the best accuracy (F1 score of 97.4%), and MobileNet the worst (F1 score of 83.84%). The MobileNet obtained the best classification speed for CPU (with a mean execution time of 102.8 ms) and GPU (72.4 ms) units. For comparison, Inception V3 achieved a mean execution time of 1058.3 ms for CPU and 634.5 ms for GPU. We conclude that the mobile application proposed can be successfully used to run mobile networks and traditional networks for image classification, but the balance between accuracy and execution time needs to be carefully assessed. This mobile app is a tool for researchers, policymakers, non-governmental organizations, and the general public who intends to assess the tree species, providing a GUI-based platform for non-programmers to access the capabilities of deep learning models in complex classification tasks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".