Using Artificial Intelligence-Based Approach for Detecting Insects-Induced Grain Damage
Bibliographic record
Abstract
According to the Intergovernmental Panel on Climate Change, around 30% of global food production is wasted each year. Prevention strategies during post-harvest grain storage may reduce food waste and help distribute food surpluses more fairly globally, ultimately reducing global hunger. Insect damage is a major cause of post-harvest storage losses, resulting in both quality and nutritional loss to the grain. Recently, data-driven approaches have been used to enhance proper and efficient storage. The aim of this project is to investigate how artificial intelligence (AI) can be used to detect insect-induced grain damage during storage. This project used barley grain to test the experimental hypothesis that insect-related damage during grain storage can be detected by simultaneous imaging and AI-based analysis. An AI approach was developed using Python to build a prediction model. Data was collected by acquiring images of insect-induced damaged and undamaged grain. The AI-based model was highly (90%) accurate in identifying the damage caused by the insect based on the parameters defined in the algorithm. In summary, this innovative approach allowed us to identify grain damage, which, in the future, will help take necessary intervention(s) to prevent insect-induced grain damage and ultimately prevent loss during post-harvest storage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".