Performance test of a half-feed peanut-cleaning picker suitable for clay hill areas in southern China
Bibliographic record
Abstract
To solve the problems of excessive soil lumps, broken branches and seedlings in pods and difficulty in removing impurities in the harvested pods of peanuts in the hilly areas of southern China, which is due to the high soil viscosity and easy hardening, and to improve the soil removal effect of the peanut half-feed pod picker in hilly areas of China, this article designed a half-feed peanut-cleaning picker suitable for southern clay hilly areas and provided a detailed introduction to the overall structure, working principle, and adjustment methods of various operating parameters. Through field measurement experiments, the influencing factors and laws of soil removal performance indicators, such as the comprehensive soil removal rate and POD drop rate, were studied. The results showed that the comprehensive soil removal rate gradually increased with increasing soil patting frequency, soil patting amplitude, and picking roller speed, first increased and then decreased with increasing picking roller gap and gradually decreased with increasing soil patting plate gap and clamping conveyor chain speed. The comprehensive soil removal rate of the soil patting plate using the opposite patting method was greater than that of the swinging patting method. The soil removal pod drop rate increased gradually with increasing soil patting frequency and amplitude and decreased with increasing soil patting plate gap and clamping conveyor chain speed. The soil removal pod drop rate of the soil patting plate using swing patting was lower than that using the opposite patting method. The speed and gap of the picking roller had no effect on the soil removal pod drop rate. The frequency, amplitude, form, and gap of the soil patting plate had extremely significant impacts on the rate of decrease in the number of soil removal pod drop rate. The clamping conveyor chain speed had a significant impact, while the speed and gap of the picking roller had no significant impact. The influence of all factors on the comprehensive soil removal rate was extremely significant. This study provides a theoretical basis and technical reference for parameter optimization research on peanut-cleaning picking machines in the clay hilly areas of southern China. Key words: peanut; southern clay hilly areas; picker; comprehensive soil removal rate; soil removal pod drop rate DOI: 10.25165/j.ijabe.20241704.8782 Citation: Wang S Y, You Z Y, Sun D W, Xiao Q, Luo Q J, He L. Performance test of a half-feed peanut-cleaning picker suitable for clay hill areas in southern China. Int J Agric & Biol Eng, 2024; 17(4): 156–164.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".