Hou Zhaolong, Wang Yaxuan, Tan Feng, Gao Jiaxin, Jiao Feng, Su Chunjie, Zheng Xin
College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
College of Civil Engineering and Water Conservancy, Heilongjiang Bayi Agricultural University, Daqing 163319, China.
Plants (Basel). 2025 Apr 12;14(8):1199. doi: 10.3390/plants14081199.
Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy.
准确诊断作物营养状况对于现代农业优化产量和品质至关重要。本研究提高了基于拉曼光谱的营养诊断准确性，改善了其在精准农业中的应用。我们提出一种方法来确定黄瓜叶片上的最佳诊断位置，以便早期检测氮（N）、磷（P）和钾（K）缺乏情况，从而为使用拉曼光谱（RS）进行高通量表型分析提供有力的科学依据。我们采用点阵方法，在不同叶片位置收集RS数据，并通过光谱余弦相似性分析探索诊断位置的选择。这些结果为开发用于作物营养胁迫监测的快速、无损方法提供了关键见解。结果表明，在叶片发育早期或短期（24小时）营养胁迫下，各位置间的光谱相似性表现出更高的不稳定性，各处理组间光谱数据稳定性存在显著差异。然而，对相似性值较低的位置进行空间分布的可视化分析发现，不同处理组间的光谱相似性分布模式一致，相似性值较低的情况主要出现在叶缘、主脉附近和叶基部。排除低相似性数据显著提高了早期（24小时）营养缺乏诊断的模型性能，从而获得更高的精度、召回率和F1分数。基于这些结果，所提出的诊断位置选择方法的有效性得到了验证。建议在诊断植物早期营养缺乏时，避免从叶缘、主脉和叶基部附近区域收集RS数据，以提高诊断准确性。
