A Lightweight Greenhouse Tomato Fruit Identification Method Based on Improved YOLOv11n
Bibliographic record
Abstract
The aim of this paper is to propose an improved lightweight YOLOv11 detection method in response to the difficulty of extracting tomato fruit features in greenhouse environments and the need for lightweight picking equipment. Firstly, the conventional step convolution is substituted by the Average pooling Downsampling (ADown) module with multi-path fusion; Gated Convolution (gConv) is incorporated in the C3K2 module, which considerably reduces the number of parameters and computation of the model. Concurrently, the Lightweight Shared Convolutional Detection (LSCD) is incorporated into the detection head component with to the aim of further reducing the computational complexity. Finally, the Wise–Powerful intersection over Union (Wise-PIoU) loss function is employed to optimise the model accuracy, and the effectiveness of each improvement module is verified by means of ablation experiments. The experimental results demonstrate that the precision of ACLW-YOLO (A stands for ADown, C stands for C3K2_gConv, L stands for LSCD, and W stands for Wise-PIoU) reaches 94.2%, the recall (R) is 92.0%, and the mean average precision (mAP) is 95.2%. Meanwhile, the model size is only 3.3 MB, the number of parameters is 1.6 M, and the floating-point computation is 3.9 GFLOPs. The ACLW-YOLO model enhances the precision of detection through its lightweight design, while concurrently achieving a substantial reduction in computational complexity and memory utilisation. The study demonstrates that the enhanced model exhibits superior recognition performance for various tomato fruits, thereby providing a robust theoretical and technical foundation for the automation of greenhouse tomato picking processes.
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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 | 0.001 |
| 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 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".