IoT-Enabled Modified XGBoost Approach for Ripeness Detection and Classification of Bananas in Smart Agriculture
Bibliographic record
Abstract
The ripening stage determination for the climacteric fruit banana bears great importance in terms of its medicinal and food values with good commercialisation. The ripening of banana fruit incurs huge loss specially in case of transit , shipping and storage . The large-scale handling of the fruits leads to the bulk loss. The biochemical process is the effective one for such determination. Most of such procedures are invasive, intrusive, harmful and create some false interpretation due to insufficiency of illumination during different times of day. In the present study the texture features through GLCM corresponding to different phases of ripening in banana species has been examined. The IOT sensor interpreted aromatic data difference, its classification and prediction of rotting has been carried out. The modified XGBoost algorithm optimised by the modified Grid search algorithm has been implemented where the enumeration of the split points based on the 1st and 2nd order gradient has been carried out. On the basis of split score value the gain has been estimated and the child on left and right has been assigned. For the grid search model, the latin hypercube concept has been used. Feature vectors of approximately 2240 different samples have been prepared in training the model with the achievement of accuracy value of 98%. The classification result for the banana cultivars Martaman, Singapuri and Kathali in the present study has been compared to mathematical models based on ripening characteristics along with other traditional models. The result shows considerable improvement confirming potential aspects of the proposed model towards smart farming.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".