pith:HCPJE4IA
Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations
CNN-based detectors deliver comparable weed detection accuracy to transformers but at far lower computational cost.
arxiv:2605.00908 v2 · 2026-04-29 · cs.CV
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Record completeness
Claims
The results highlight a clear trade-off between efficiency and contextual modeling: CNN-based detectors achieve high performance at a lower computational cost, while transformer-based approaches offer better global context capture at the expense of higher resource demands.
The GROUNDBASED_WEED dataset adequately represents realistic early-weed scenarios in precision agriculture, and the selected models (YOLOv26-nano, RTDETR, RF-DETR) are fair representatives of the convolutional and transformer paradigms.
CNN-based detectors like YOLOv26-nano deliver high weed detection accuracy at lower computational cost than transformer models like RTDETR and RF-DETR on the GROUNDBASED_WEED dataset.
Receipt and verification
| First computed | 2026-05-26T01:02:34.473909Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
389e927100c4bedb6dfbb3e8502e2477bbaeb3743a6b4166c43602b7a7ad7d48
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/HCPJE4IAYS7NW3P3WPUFALREO6 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 389e927100c4bedb6dfbb3e8502e2477bbaeb3743a6b4166c43602b7a7ad7d48
Canonical record JSON
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