Weed Identification using Ultrasonic Sensor in Labview
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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