Deep Learning Model Reaches 98.39% Accuracy in Cotton Leaf Disease Classification
A hybrid ConvNeXt and Random Walk Grey Wolf Optimizer framework classified six categories of cotton leaf conditions with 98.39% accuracy ...
A hybrid ConvNeXt and Random Walk Grey Wolf Optimizer framework classified six categories of cotton leaf conditions with 98.39% accuracy ...
A new deep learning framework detects species-specific microscopic structures in orchid stem powder with 93.35 percent mean average precision across ...
A peer-reviewed experimental review in Artificial Intelligence Review examines how image enhancement, scaling, and evaluation metrics jointly determine the reliability ...
A hybrid deep learning framework that fuses handcrafted morphological measurements with convolutional image features identified two destructive root-knot nematode species ...
A convolutional neural network published in Scientific Reports estimates residual saturation to rebalance color channels and a transmission network to ...
A new non-iterative neural network called DNet-MRSP injects multi-resolution speckle priors into a U-Net decoder, achieving 0.00302-pixel displacement accuracy at ...
A semi-supervised YOLO11 pipeline using confidence filtering, DIoU-NMS deduplication, and light manual checks outperforms fully supervised scrap steel detection while ...
A systematic experimental study in Artificial Intelligence Review finds detector improvements deliver more than 10 percent tracking gains while refined ...
A newly published two-stage deep network called FADFNet uses parallel feature extraction, source-image compensation, and a decomposition feedback loop to ...
A lightweight machine-vision prototype pairs a single industrial camera with a streamlined detector called MOD-DETR and a SORT-based tracker called ...
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© 2025 Scienmag - Science Magazine