Segmentation of mycotoxin's contamination in maize: A deep learning approach
Bibliographic record
Abstract
Maize is the main staple food and feed inSub-Saharan African countries and is highly susceptible to mycotoxin contamination under opportune environmental conditions. The presence of mycotoxins in maize affects the health of consumers and impacts global trade. According to the literature, the lack of mycotoxin awareness and the existence of strategies that are labor- and cost-prohibitive have led to the ongoing mycotoxin contamination in maize. Therefore, this study developed a cost-effective deep learning-based mobile application for segmentation of mycotoxin contamination in maize; using the RESNET152 model with performance rates of accuracy, test accuracy, epochs, time used, loss and image size results at 99.5%, 99.9%, 40, 07:30 min, and 0.051; and 460 respectively and performance evaluation metrics of F1-Score and sensitivity 0.62 and 0.997 respectively. During, the development processes, a total of 4800 images were collected and augmented. Then, the resulting 9600 data points were randomly shuffled and then split into the ratio of 70%:20:10% for training, validation, and testing datasets in order to avoid overfitting and biases in the resulting model. Lastly, the average result of model validation was 89% which was conducted among the farmers in the Maize area, Maize entrepreneurs, ICT experts, decision-makers from the Government, and policymakers. Therefore, the study recommends the collection of quality data which can be in the form of images, satellite, and biochemical properties of mycotoxin in order to enable researchers to analyze the contamination of mycotoxin and its linkages with environmental factors such as weather, soil characteristics, geographical position, and other unexpected events.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".