Evaluation of CNN Models in Identifying Plant Diseases on a Mobile Device
Bibliographic record
Abstract
Farmers in rural areas with limitation of internet connectivity can be made possible for early plant diseases detection by using optimization of mobile devices which implemented an application based on Convolutional Neural Network (CNN) because of the computational efficiency.The researchers used a dataset containing 79 different classes of plant which was merged from several public domain datasets, which was evaluated and compared using four CNN models, consisting of MobileNetV3, EfficientNetB0, Mason model, and ShuffleNetV2.The experiment results showed that Mason model has a highest accuracy of 90.54% and the smallest output file of 0.85MB, MobileNetV3 88.83% with 16.85MB, EfficientNetB0 88.75% with 16.08MB, and ShuffleNetV2 83.52% with 15.89MB, which the four models have a slight accuracy decrease on both workstation and mobile devices.However, on resource consumption overall, MobileNetV3 consumed less than the others model, even though the value hasn't a huge difference of several points.It can be concluded that Mason model is the most suitable model to be implemented on mobile devices because of accuracy and low resource consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.000 |
| 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".