Smart Agriculture and AI has 704 eligible papers in the latest 30-day evidence window, roughly steady against the prior window, with representative work spanning PlantShield AI: An Integrated Deep Learning Framework for Intelligent Crop Pest and Disease Detection in Precision Agriculture; DGS-Net: A Lightweight Deformable and Occlusion-Aware Network for Paddy Weed Detection on Edge Devices; Swin-HViT for accurate crop disease prediction using an adaptive hybrid transformer model.
Smart Agriculture and AI shows 704 eligible recent papers and 704 commentary-ready papers in the current 30-day window, compared with 674 eligible papers in the prior window. The strongest evidence comes from 1 visible topic cluster and 8 representative papers. Several representative papers may be preprints, so this brief treats them as emerging signals rather than settled consensus.
Smart Agriculture and AI recorded 704 eligible papers in the latest 30-day window, compared with 674 in the prior 30-day window, making the current snapshot roughly steady against the prior window.
704 recent vs 674 prior eligible papersSmart Agriculture and AI contributes 704 eligible recent papers, including 704 papers with abstracts available for commentary.
704 papers in the leading clusterThe representative set includes PlantShield AI: An Integrated Deep Learning Framework for Intelligent Crop Pest and Disease Detection in Precision Agriculture; DGS-Net: A Lightweight Deformable and Occlusion-Aware Network for Paddy Weed Detection on Edge Devices; Swin-HViT for accurate crop disease prediction using an adaptive hybrid transformer model; SEMI-SUPERVISED MAIZE SEEDLING SEMANTIC SEGMENTATION METHOD BASED ON VISION TRANSFORMER AND CURRICULUM LEARNING; and other recent papers. These papers anchor the page's claims and keep the brief tied to visible evidence.
8 representative papersSmart Agriculture and AI accounts for 704 eligible recent papers, including 704 commentary-ready papers in this evidence window.
704 recent eligible papersThe selected papers cover PlantShield AI: An Integrated Deep Learning Framework for Intelligent Crop Pest and Disease Detection in Precision Agriculture; DGS-Net: A Lightweight Deformable and Occlusion-Aware Network for Paddy Weed Detection on Edge Devices; Swin-HViT for accurate crop disease prediction using an adaptive hybrid transformer model; SEMI-SUPERVISED MAIZE SEEDLING SEMANTIC SEGMENTATION METHOD BASED ON VISION TRANSFORMER AND CURRICULUM LEARNING.
8 representative papersA recent paper from International Journal for Research in Applied Science and Engineering Technology in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from Agriculture in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from Discover Artificial Intelligence in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from INMATEH Agricultural Engineering in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from Frontiers in Agronomy in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from Agriculture in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.
A recent paper from ROBOMECH Journal in the Smart Agriculture and AI evidence packet, selected because it is recent, abstract-backed, and representative of this week's topic activity.