用多光谱成像+改进版YOLO检测植物缺素，精度提升12%。
Analysis of Plant Nutrient Deficiencies Using Multi-Spectral Imaging and Optimized Segmentation Model
- 融合多光谱图像与注意力机制，增强对细微病征的识别能力。
- 在9通道输入下，平均Dice和IoU提升约12%，显著优于基线模型。
- 适合农业科研、智慧种植系统开发者参考应用。
精准检测植物叶片营养缺乏对精准农业至关重要，有助于早期开展施肥、病害与胁迫管理。本研究提出一种基于多光谱成像与优化YOLOv5模型的叶片异常分割框架。该模型针对九通道多光谱输入设计，引入基于Transformer的注意力头，更有效捕捉空间分布细微的症状。实验在受控营养胁迫条件下培育的植物上进行。大量对比实验表明，所提模型显著优于基线YOLOv5，平均Dice分数与交并比（IoU）提升约12%。尤其在检测叶绿素缺失（chlorosis）和色素积累等挑战性症状方面表现优异。结果表明，结合多光谱成像与光谱-空间特征学习，在推进植物表型分析与精准农业方面具有巨大潜力。
原文摘要 · Abstract (English)
Accurate detection of nutrient deficiency in plant leaves is essential for precision agriculture, enabling early intervention in fertilization, disease, and stress management. This study presents a deep learning framework for leaf anomaly segmentation using multispectral imaging and an enhanced YOLOv5 model with a transformer-based attention head. The model is tailored for processing nine-channel multispectral input and uses self-attention mechanisms to better capture subtle, spatially-distributed symptoms. The plants in the experiments were grown under controlled nutrient stress conditions for evaluation. We carry out extensive experiments to benchmark the proposed model against the baseline YOLOv5. Extensive experiments show that the proposed model significantly outperforms the baseline YOLOv5, with an average Dice score and IoU (Intersection over Union) improvement of about 12%. In particular, this model is effective in detecting challenging symptoms like chlorosis and pigment accumulation. These results highlight the promise of combining multi-spectral imaging with spectral-spatial feature learning for advancing plant phenotyping and precision agriculture.
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