Transformer-based detector spots camouflaged soybean pests more accurately
An enhanced RT-DETR-ResNet18 framework raises detection accuracy for camouflaged soybean pests, reaching 77.4% mAP@0.5 across 12 pest categories.
An enhanced RT-DETR-ResNet18 framework raises detection accuracy for camouflaged soybean pests, reaching 77.4% mAP@0.5 across 12 pest categories.
A validated 32-item questionnaire measuring digital transformation capabilities and maturity stages in smallholder farmers has been tested with 68 potato ...
Researchers have validated a colorimetric method that measures urea and ammonium simultaneously in a single KCl extract and applied it ...
Fall armyworm, the voracious caterpillar that has become one of the most destructive pests of maize worldwide, can be pushed ...
A lightweight hybrid transformer model combining wavelet transforms with MobileViT achieved over 99% accuracy in classifying plant diseases across four ...
A new BioScience article led by Mary Ann Ottinger introduces the journal's One Health Special Issue, organizing the field's challenges ...
A new policy brief maps how Europe's farms could serve older adults, urging systematic collaboration between public health and agriculture.
Salicylic acid has long been celebrated as the master switch of plant immunity, a hormone that lets crops detect pathogens ...
A hybrid deep learning framework that fuses handcrafted morphological measurements with convolutional image features identified two destructive root-knot nematode species ...
A comprehensive review maps how hydrogen-rich water is produced, why its dissolved hydrogen is so hard to keep, and what ...
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© 2026 Scienmag - Science Magazine