为了更精确地诊断水稻的磷素营养状况，进而促进水稻的健康生长，我们提出了一种基于改进ShuffleNet V2的水稻磷素营养诊断方法。该方法的核心是在ShuffleNet V2网络模型引入ECA（Efficient Channel Attention）注意力机制，以优化原有模型。同时，选用注意力池化（Attention Pooling）技术来进一步提升模型训练的效果。在模型训练过程中，采用了迁移学习策略，即将在大规模数据集ImageNet上预训练的权值迁移至经过改进的ShuffleNet V2网络模型中，并利用这些权值对水稻叶片数据集进行训练，从而构建出水稻磷素营养诊断模型。结果显示，相比其他对比的网络结构模型，改进后的ShuffleNet V2网络模型在水稻分蘖期和拔节期的准确率、精确率、召回率以及F1值均表现出更高的水平，且该模型训练参数少、训练过程更稳定、收敛速度更快，证明改进后的ShuffleNet V2水稻磷素营养诊断模型具备了出色的诊断识别能力，能够为大数据背景下的科学、有效施肥策略提供有力支持。改进的ShuffleNet V2网络模型在Plant Village公共数据集上也取得显著效果，验证了其有效性和良好的泛化能力。

In order to more accurately diagnose rice phosphorus nutrition and help rice growth, an improved ShuffleNet V2 rice phosphorus nutrition diagnosis method is proposed. This method improves the model by introducing the ECA attention mechanism into the ShuffleNet V2 network model, and selects the pooling method of Attention Pooling to optimize model training. The transfer learning strategy is adopted to transfer the pre-trained weights on the ImageNet large dataset to the improved ShuffleNet V2 network model to train the rice leaf dataset and construct a rice phosphorus nutrition diagnosis model. The experimental results showed that the improved ShuffleNet V2 network model had higher accuracy, precision, recall and F1 value than other comparative network structure models in the rice tillering stage and rice jointing stage, and the training parameters were small, the training was more stable, and the convergence speed was faster. It proved that the improved ShuffleNet V2 rice phosphorus nutrition diagnosis model had good diagnostic recognition ability, which was helpful to adopt scientific and effective fertilization strategies under big data. At the same time, the improved ShuffleNet V2 network model had also achieved remarkable results on the Plant Village public dataset, which further verified the effectiveness and generalization ability of the improved model.