Bibliographic record
Abstract
Automatically distinguishing different types of plant images is a challenging problem relevant to both Botany and Computer Science disciplines. Plant identification at the species level is a computer vision task called fine-grained categorization, which focuses on differentiating between hard-to-distinguish object classes. This classification problem is complicated and challenging because of the lack of annotated data, inter-species similarity, the large-scale features in appearance, and a large number of plant species. A plant classification system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in public computer science education but also in numerous agricultural activities such as automatic detection of cash crops and non-crop plants (called weeds). Furthermore, successful automation of crop and weed identification will lead to the reduction of chemical compounds currently used to eliminate weeds [15]. Deep Convolutional Neural Networks (CNN) can be a solution to perform this computer vision task. In this thesis, seven different CNN models are deployed to classify 1 million images - from the TerraByte dataset - of eleven very similar plant species [13]. This robust approach divides the problem into two main steps: the first step, called the generalist, identifies similar plants and separates them into different groups that contain indistinguishable plant species. The second step, called specialist, is used to classify plants within the groups of indistinguishable plants, including five weed and seven crop species, with high accuracy. The generalist-specialist CNN network shows that the hierarchical network outperforms simple CNN models in terms of accuracy and classifying similar plant images. The contributions of this thesis are the explored different CNN models and improved performance of those models by designing and implementing the generalist-specialist CNN models for classifying similar plant images.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".