Detection of Curved Rows and Gaps in Aerial Images of Sugarcane Field Using Image Processing Techniques
Why this work is in the frame
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Bibliographic record
Abstract
Sugarcane is one of the main crops in the world due to its economic value promoted by the sale of its derivatives, such as bioethanol and sugar. In order to achieve greater economic performance and productivity in the sugarcane field, several digital image processing studies have been conducted on sugarcane field images. However, mapping and measuring gaps in the planting rows are still being performed manually on-site to determine whether to replant the entire area or only the gaps. High cost of time and manpower is required to perform the manual measurement. Based on that, the aim of this study is to present a novel method to detect crop rows and measure gaps in crop fields. Our method is also able to deal with curved crop rows, which is a real problem and substantially limits numerous solutions in practical applications. The proposed method is evaluated using a mosaic of real scene image that was prepared with the support of a small remotely piloted aircraft. Experimental tests showed a low relative error of approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.65$</tex-math> </inline-formula> % compared to manual mapping in the planting regions, even for regions with gaps in the curved crop rows. It means that our proposal can identify and measure crop rows accurately, which enables automated inspections with high-precision measurements.
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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