Digital Seed Morphometric Analysis of Nigerian Cultivated Rice (<i>Oryza sativa</i> L.) Varieties Grown Under Guinea Savannah Agro-ecology
Bibliographic record
Abstract
This study investigated the varietal differences for morphometric properties among Nigerian cultivated rice varieties. Twenty-two rice varieties were grown under rain fed conditions in guinea agro-ecology at National Agricultural Seed Council, Abuja, Nigeria in 2017 and 2018. After 30 days of harvest, seed samples were collected for morphometric evaluation. The seed samples were evaluated in laboratory of the Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta in 2017 and 2018 using completely randomized design in three replicates. Seeds obtained from the 22 varieties in two years were assessed for: six morphometric (physical) characters: seed projected area, seed straight length (mm), seed curve length (mm), seed straight width (mm), seed curved width (mm), seed width length (mm) and seed perimeter (mm) using an Epson Scanner connected to a computer device to acquire image of the seeds. A reagent instrument from Reagent Instrument Inc. Canada was used for the digital image analysis by running the custom written software WinSEEDLETM (Pro Version). Data obtained were subjected to Analysis of Variance and means were separated using Tukey’s HSD at 5% probability level. Pearson’s correlation coefficient and principal component analyses were also used. Significant varietal differences were observed for all seed physical characters evaluated. Seed physical characters (projected area, curve length, seed width, seed length and perimeters) were higher in 2018 compared to 2017. WAB 189 had superior physical characters. FARO 62 and FARO 22 had the least values for most of the seed physical characters. Most of the seed morphometric characteristics were strongly associated with one another. PC1 with seed projected area, straight length, seed straight width, seed curved width and seed perimeter contributed to the total variation observed. The study concluded that WAB 189 and FARO 50 with superior seed physical characters should be used for future seed improvement programme.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".