KDOSS-net: Knowledge distillation-based outpainting and semantic segmentation network for crop and weed images
Why this work is in the frame
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Bibliographic record
Abstract
Weed management plays a crucial role in increasing crop yields. Semantic segmentation, which classifies each pixel in an image captured by a camera into categories such as crops, weeds, and background, is a widely used method in this context. However, conventional semantic segmentation methods rely solely on pixel information within the camera’s field of view (FOV), hindering their ability to detect weeds outside the visible area. This limitation can lead to incomplete weed removal and inefficient herbicide application. Incorporating information beyond the FOV in crop and weed segmentation is therefore essential for effective herbicide usage. Nevertheless, existing research on crop and weed segmentation has largely overlooked this limitation. To address this issue, we propose the knowledge distillation–based outpainting and semantic segmentation network (KDOSS-Net) for crop and weed images, a novel framework that enhances segmentation accuracy by leveraging information beyond the FOV. KDOSS-Net consists of two parts: the object prediction–guided outpainting and semantic segmentation network (OPOSS-Net), which serves as the teacher model by restoring areas outside the FOV and performing semantic segmentation, and the semantic segmentation without outpainting network (SSWO-Net), which serves as the student model, directly performing segmentation without outpainting. Through knowledge distillation (KD), the student model learns from the teacher’s outputs, which results in a lightweight yet highly accurate segmentation network that is suitable for deployment on agricultural robots with limited computing power. Experiments on three public datasets—Rice seedling and weed, CWFID, and BoniRob—yielded mean intersection over union ( mIOU ) scores of 0.6315, 0.7101, and 0.7524, respectively. These results demonstrate that KDOSS-Net achieves higher accuracy than existing state-of-the-art (SOTA) segmentation models while significantly reducing computational overhead. Furthermore, the weed information extracted using our method is automatically linked as input to the open-source large language and vision assistant (LLaVA), enabling the development of a system that recommends optimal herbicide strategies tailored to the detected weed class.
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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