Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse
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Bibliographic record
Abstract
The production of greenhouse-grown sweet pepper (Capsicum annuum L.) is irregular with a peak-and-valley pattern of weekly yields. We monitored the yields and environment in a commercial greenhouse in British Columbia over six (2000–2005) growing seasons. Light was defined as cumulative light over the current week, with L_1, L_2, L_3, L_4, L _ 5 and L_6 representing light over previous weeks. Temperature (AvgT) was defined as the current weekly average of 24-h air temperatures, with T_1, T_2 and T_3 representing temperatures over previous weeks. Inputs were also created for the current weekly yield (Y) and previous weekly yields (Y_1, Y_2, Y_3 and Y_4). Neural network (NN) modelling with up to 21 inputs was used to predict yields 1 wk (Y + 1) and 2 wk (Y + 2) in advance of the actual fruit harvest. Data for five different years were combined for model training with the year to be predicted held separate as a validation set. The best models used 13 inputs to predict Y + 1 with an average R 2 of 0.66 over the 6 yr. Y_4, Y-Y_1, Y_1, L_1, Y, Y_3, Y-Y_3 and wk (of the year) were important inputs. The environmental inputs were of lesser importance, which suggests that the cyclic nature of pepper yields is inherent in the pepper biology. Predicting Y + 2 was more difficult with an average R 2 of 0.59 over the 6 yr. NN have good potential for predicting pepper yields. Key words: Capsicum annuum L., flushing, fruit, greenhouse production, neural networks
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it