Tag: plant disease detection

A narrative review synthesizes how nanosensors, nanoimaging and machine-learning pipelines could enable earlier crop disease detection, while flagging unresolved gaps ...

A self-supervised CNN framework with Grad-CAM heatmaps reached up to 94.38% validation accuracy on a 2,137-image cotton leaf disease dataset.

A new deep learning pipeline raises yellow rust classification accuracy on wheat leaves from 78 to 87 percent on the ...

A new study from Bahawalpur, Pakistan, finds that transformer models, led by the Swin Transformer, classify cotton leaf diseases more ...

A lightweight hybrid transformer model combining wavelet transforms with MobileViT achieved over 99% accuracy in classifying plant diseases across four ...

A hybrid vision transformer framework called ViT-LGFNet combines convolutional local texture extraction with global self-attention, reaching 95.15% mean validation accuracy ...

A systematic review of 59 studies finds deep learning dominates fungal leaf disease detection while bacterial diagnosis, generative AI and ...

A hybrid DeiT system reached 99.13% accuracy on a 38-class plant disease benchmark while combining local and global AI explanations ...

A hybrid ConvNeXt and Random Walk Grey Wolf Optimizer framework classified six categories of cotton leaf conditions with 98.39% accuracy ...

A multimodal deep learning framework combining RGB photographs, hyperspectral reflectance, and SPAD chlorophyll readings detected molecularly confirmed symptomless citrus huanglongbing ...