Weed Identification using Ultrasonic Sensor in Labview
Bibliographic record
Abstract
The presence of weeds and pests in the crop field is a common phenomenon.The success of site-specific pest management depends on accurate identification of the pest and weeds in crop field.An innovative low-cost ultrasonic sensor system was developed to detect weeds and bare spots in wild blueberry cropping system.Ultrasonic sensors were mounted besides the rear wheels of the specially designed Farm Motorized Vehicle.Trimble Ag GPS 332 was mounted above the sensors to locate the exact locations of sensor data points for mapping.Custom software interface was developed in Lab View 8.5 to collect and store the sensor data along with DGPS co-ordinates in a laptop computer.The ultrasonic system calibrated using the fixed height objects in the laboratory and vegetation in the wild blueberry fields.Linear regression analysis showed significant relationship between actual heights and sensor heights (R 2 = 0.98).The survey of the field for weeds and bare spots detection was carried out at a speed of 0.54 m s -1 .The height maps were generated in Arc View 3.2 showing weed patches, bare-spots and wild blueberry plants in selected fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".