AD-DETR: A Real-Time Transformer with Multi-Scale Alignment and Spatial-Spectral Fusion for Crop Disease Detection. [PDF]
Wang B, Zhou H, Wang Z, Chen R.
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DDHTS-Net: dual-domain hierarchical texture supervision network for plant texture analysis. [PDF]
Li B, Guo E, Xu X, Wu QMJ.
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Deep convolutional models for robust multi-crop disease recognition in real-world conditions. [PDF]
Srivastava A +6 more
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Optimized Lightweight U-Net and YOLACT framework for multi-disease severity detection in pome fruit leaves. [PDF]
Qasim M, Adnan SM, Safi QGK.
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ConvGeM-next: a deep learning framework for plant disease detection. [PDF]
Arshad Z, Javed A, Saudagar AKJ.
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YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision. [PDF]
Makhmudov F +8 more
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YOLO11-SBS: leveraging ACE and ORCM to gauge robust and accurate apple counting in complex orchards. [PDF]
Zhang L +8 more
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Deep learning for apple leaf disease diagnosis: a comparative study with convolutional neural networks and transformers. [PDF]
Toutounchian S +4 more
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Genome-wide association study combined with multi-assay phenotyping identifies a novel anthracnose resistance locus in apple. [PDF]
Kwon D, Choi BG, Park JT, Ban S, Choi C.
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Climatic transferability and regional validation of a PAT-based mechanistic model for apple scab ascospore maturation under temperate Himalayan conditions. [PDF]
Bashir A +7 more
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