AI pipeline sharpens detection of yellow rust on wheat leaves
A new deep learning pipeline raises yellow rust classification accuracy on wheat leaves from 78 to 87 percent on the ...
A new deep learning pipeline raises yellow rust classification accuracy on wheat leaves from 78 to 87 percent on the ...
A new deep learning model called MSAF-Net detects drought and salinity stress in crops with 96.82% accuracy and outperforms existing ...
A new study from Bahawalpur, Pakistan, finds that transformer models, led by the Swin Transformer, classify cotton leaf diseases more ...
A biodegradable microneedle patch made of polyvinyl alcohol, polyaniline and phytic acid monitors glucose in living plant leaves for 14 ...
Engineers in Pakistan have built and field-tested a solar-powered, IoT-connected agricultural robot that digs, sows, irrigates and cuts crops, achieving ...
A hybrid vision transformer framework called ViT-LGFNet combines convolutional local texture extraction with global self-attention, reaching 95.15% mean validation accuracy ...
A combined simulation and field study identified a stepwise pipeline mixing design and a 75% slurry concentration with 75% field ...
Machine-learning forecasts of air and leaf temperature in a commercial tomato greenhouse detected below-setpoint thermal events with average warning lead ...
A systematic review of IoT and AI-enabled microclimate regulation in poultry farming demonstrates that predictive climate management can achieve thermal ...
A lightweight YOLOv8n-based model reached 0.980 mAP@0.5 for seven tea pest and disease classes while substantially reducing model size and ...
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© 2025 Scienmag - Science Magazine