Recognition and Detection of Greenhouse Tomatoes in Complex Environment
Bibliographic record
Abstract
In the complex environment of greenhouses, it is important to provide the picking robot with accurate information. For this purpose, this paper improves the recognition and detection method based on you only look once v5 (YOLO v5). Firstly, adding data enhancement boosts the network generalizability. On the input end, the k-means clustering (KMC) was utilized to obtain more suitable anchors, aiming to increase detection accuracy. Secondly, it enhanced multi-scale feature extraction by improving the spatial pyramid pooling (SPP). Finally, non-maximum suppression (NMS) was optimized to improve the accuracy of the network. Experimental results show that the improved YOLO v5 achieved a mean average precision (mAP) of 97.3%, a recall of 90.5%, and an F1-score of 92.0%, while the original YOLO v5 had a mAP of 95.9% and a recall of 85.6%; the improved YOLO v5 took 57ms to identify and detect each image. The recognition accuracy and speed of the improved YOLOv5 are much better than those of faster region-based convolutional neural network (Faster R-CNN) and YOLO v3. After that, the improved network was applied to identify and detect images take in unstructured environments with different illumination, branch/leave occlusions, and overlapping fruits. The results show that the improved network has a good robustness, providing stable and reliable information for the operation of tomato picking robots.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".