{"paper":{"title":"Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"CNN-based detectors deliver comparable weed detection accuracy to transformers but at far lower computational cost.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alcides Toledo Espinosa, \\'Angel Eduardo Zamora-Su\\'arez, Gerardo Antonio \\'Alvarez Hern\\'andez, Juan Irving V\\'asquez, Miguel Bola\\~nos","submitted_at":"2026-04-29T08:23:28Z","abstract_excerpt":"This paper presents a comparative evaluation of convolutional and transformer-based object detection architectures for early weed detection in tomato plantations. Representative models from each paradigm are considered, including YOLOv26-nano, a recent variant of the YOLO family, and RT-DETR Large and RF-DETR Medium as transformer-based architectures. The evaluation was conducted on the GROUNDBASED_WEED dataset, considering six weed classes and an additional category corresponding to unidentified plants, which allowed for the assessment of performance in terms of detection accuracy and computa"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"CNN-based detectors deliver comparable weed detection accuracy to transformers but at far lower computational cost.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"21ace0d0d03afb315b21a3862a9a2570746c10fd9f38abaecc1503077edad71b"},"source":{"id":"2605.00908","kind":"arxiv","version":2},"verdict":{"id":"22ed41d5-9b87-4d57-b0ad-30624ec212cb","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T20:04:18.160831Z","strongest_claim":"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.","one_line_summary":"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.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"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.","pith_extraction_headline":"CNN-based detectors deliver comparable weed detection accuracy to transformers but at far lower computational cost."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.00908/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T00:37:17.240493Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:11:35.245568Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"3909ed50607dec35810588981ad437bec5043f9e18305ffba85b458b60d723cf"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}