Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers
Bibliographic record
Abstract
Sweet peppers (Capsicum annuum L.) grown in the greenhouse have irregular yields. Modelling colouration of individual fruit could help growers predict the number of fully coloured peppers that will be ready to harvest within a routine harvest period. We monitored the red, green and blue colour intensities of developing pepper fruit via digital image processing. These colour measurements together with crop phenology and environmental variables were used as inputs into neural network (NN) models to predict days-to-harvest (D-to-H) for ind ividual fruit. When 18 inputs were evaluated, a typical “best” NN model needed only five of the inputs to predict D-to-H (range 0 to 28 d) for red peppers with a R2 of 0.79, a root mean square error (RMSE) of 3.4 d, and an average absolute error (AAE) of 2.5 d. D-to-H were more difficult to predict for yellow peppers, with the “best” model using eight inputs to achieve a R2 of 0.69, a RMSE of 4.4 d, and an AAE of 3.4 d. Light and temperature made little contribution to predictions of D-to-H. NN models with o nly three inputs (Julian day, nodal position of the target fruit and ratio of red:green intensities) could still make useful predictions of harvest maturity. For both red and yellow peppers, the R2 values of NN models were higher than the corresponding R2a (R2 adjusted) values derived from multiple linear regression models. It is concluded that NN have potential to assist greenhouse operators to predict D-to-H of sweet peppers. Key words: Greenhouse production, fruit, colouration, digital imaging, neural networks
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".