Improved YOLO-based real-time brinjal detection algorithm for vision modules in harvesting robots
Why this work is in the frame
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Bibliographic record
Abstract
Abstract A novel, lightweight, and accurate brinjal detection algorithm, YOLOv11s-Brinjal, was developed for vision modules in selective harvesting robots operating under complex horticultural environments. The algorithm addressed critical detection challenges, including variable lighting, spotlight effects, object overlap, occlusion, and cluttered backgrounds in unstructured farm settings. Multiple configurations from YOLOv8 to YOLOv12 were initially evaluated using a custom dataset, manually annotated and augmented through the Roboflow framework. The best-performing base model, YOLOv11s, was further optimized via systematic channel dimension pruning applied to the convolutional layers of its backbone architecture, significantly reducing both parameter count and computational load. To mitigate performance degradation and ensure task-specific alignment, weight adjustment techniques were implemented during fine-tuning. The YOLOv11s-Brinjal model was evaluated using the same test datasets, demonstrating robust performance with precision, recall, F1 score, and mean average precision values of 94%, 96.6%, 95.3%, and 98.1%, respectively. To assess generalization and detect potential overfitting, a 5-fold cross-validation was conducted. Compared to the original model, the proposed pruning and weight adjustment techniques improved recall by 1.3% , while reducing parameters and computational load by over 57%. With a compact model size of 8.2 MB and an inference time of 10.1 ms, YOLOv11s-Brinjal is well-suited for integration on edge devices as the vision component in real-time selective brinjal harvesting applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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