Optimization of target detection scheme for single-bud segment sugarcane cutting machine and seed-picking scheme for planter seed meter
Bibliographic record
Abstract
Sugarcane mechanized planting technology consists of seed preparation and field planting. This study aims at the issues of easy damage to the seeds during the operation of the automatic cutting machine for single-bud segment sugarcane, lack of intelligent seed selection and calibration technology, low recognition accuracy, and the need for manual feeding of the planting machine’s seed meter which leads to seed leakage. This study, based on machine vision and deep learning, optimizes the seed calibration method and proposes an improved YoloV5-STD target detection algorithm to improve the recognition accuracy of seed characteristics and optimize the overall engineering structure. For the planting machine, a new type of hopper for the seed meter is designed using natural rubber as the base material mixed with polystyrene, and the flexible automatic seed metering mechanism is analyzed to achieve automatic feeding and seed metering. Test assessment indicators were formulated based on the enterprise standards of the Institute of Agricultural Machinery Research, Chinese Academy of Tropical Agricultural Sciences. Experimental results show that the recognition accuracy of the 2DZ-2 type single-bud segment intelligent cutting machine is ≥95%, the bud injury rate is <1.8%, the qualified rate of cutting is 95.8%, and the single-channel cutting efficiency is 64 buds/min. The 2CZD-2C type single-bud segment planter has a planting qualification rate of 96.6%, a planting efficiency of 208 buds/min, and a seed leakage rate of <2.1%. Keywords: target detection, scheme optimization, automatic seed cutting, flexible seed metering DOI: 10.25165/j.ijabe.20251805.9144 Citation: Niu Z J, Ge C, Wei L J, Song G, Hou M X, Li M, et al. Optimization of target detection scheme for single-bud segment sugarcane cutting machine and seed-picking scheme for planter seed meter. Int J Agric & Biol Eng, 2025; 18(5): 165–170.
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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 | 0.001 | 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".