Accurate assessment of phosphorus nutrition is essential for optimizing rice growth and improving fertilizer management.This study proposes a lightweight image classification model， MobileNetV3_U-Net， that integrates a MobileNetV3_large encoder with an improved U-Net decoder for efficient diagnosis of rice leaf phosphorus status.The encoder leverages pretrained weights to accelerate convergence and extracts multi-scale semantic representations， while the decoder adopts an asymmetric multi-scale upsampling strategy with skip connections to progressively fuse shallow and deep features.Unlike the conventional symmetric U-Net， the redesigned decoder adjusts channel width and spatial resolution in a compact manner， enhancing fine-grained feature characterization and sensitivity to phosphorus deficiency symptoms.The classification is completed by adaptive average pooling and a fully connected layer.On test sets from the rice tillering and jointing stages， MobileNetV3_U-Net achieved accuracies of 93.33% and 87.40%， respectively， surpassing representative lightweight models including MobileNetV3_large， GhostNet， ShuffleNetV2， and EfficientNet_b1.Furthermore， experiments on the Plant Village dataset using grape leaf images confirmed its cross-domain generalization capability.MobileNetV3_U-Net demonstrates a favorable balance between lightweight design and diagnostic accuracy.By enhancing multi-scale feature fusion through a tailored decoder structure， it provides an efficient， robust， and transferable framework for intelligent diagnosis of rice phosphorus nutrition.