With the growing demand for early monitoring of crop moisture in precision agriculture continues to grow, this article proposes a deep detection framework that integrates acoustic emission signals with hyperspectral images to address the limitations of single-modal characterization and susceptibility to noise interference in detecting drought stress in cotton. The study first constructs a multiscale time–frequency branch and a regional–global spectral branch to capture, respectively, the transient pulse characteristics induced by cavitation and the long-range dependencies of the canopy spectrum. Subsequently, a dual second-order attention module is introduced to enhance edge information and spatial high-frequency details through the synergistic use of channel gradients and structural tensors. Finally, a deep adaptive fusion mechanism is designed to achieve dynamic weighted allocation of bimodal features. Experiments show that the model achieves an overall accuracy of 97.01%, significantly outperforming the baseline models, even in the more challenging task of early detection of mild drought, the model maintains a high accuracy of 95.47%. In summary, the method proposed in this article provides a highly robust technical approach for the nondestructive diagnosis of crop water deficit.
Jing-Yao Zhang, Ke Wu, Yang Li et al.· IEEE Journal of Selected Top...· 0 citations
ABSTRACT Global agriculture is increasingly challenged by climate instability, genetic erosion, emerging pathogens and rising food demands, exposing the limitations of conventional breeding and traditional domestication strategies. Recent advances in CRISPR‐based genome editing, pangenomic, synthetic biology, artificial intelligence (AI)‐assisted breeding and predictive phenomics are transforming de novo domestication from a slow evolutionary process into a programmable framework for rational crop redesign. This review synthesises recent advances in programmable de novo domestication and highlights how crop wild relatives and underutilised germplasm can be harnessed to develop resilient, climate‐adaptive and sustainable crop systems. The integration of multiplex genome editing, pan‐genomic variation discovery, AI‐driven genomic prediction and predictive breeding enables precise engineering of key domestication traits governing plant architecture, yield potential, stress resilience and nutritional quality. Furthermore, we propose a trajectory‐based framework for programmable domestication comprising Adaptive Rescue, Agronomic Refinement and Novel Chassis Engineering, which illustrates distinct evolutionary pathways, engineering complexity and crop redesign objectives. We also examine the major system level challenges that constrain programmable domestication, including cryptic genetic variation, epistasis, gene regulatory network complexity, genotype phenotype predictability, biodiversity conservation and regulatory considerations. Collectively, programmable domestication represents a transformative shift from conventional crop improvement towards system‐level engineering of next‐generation crops, providing a strategic foundation for enhancing global food security, agricultural sustainability and environmental resilience in the face of accelerating climate change.
Muhammad Mubashar Zafar, H. Firdous, A. Siddiqua et al.· Plant Biotechnology Journal· 0 citations