| Related citation: | Xu E,Chenkao Liu,Jin Zhou,Wei Song,Qi Yan,Song Wang.Apple Leaf Disease Identification Model Based on ImprovedMobileNetV3-Small[J].Journal of Harbin Institute Of Technology(New Series),2025,32(4):18-28.DOI:10.11916/j.issn.1005-9113.24065. |
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| Abstract: |
| To enhance the recognition accuracy of current network models for apple leaf diseases, a lightweight model that leverages an enhanced MobileNetV3-Small architecture is introduced in this study. The improved model utilizes MobileNetV3-Small, a lightweight architecture with fewer parameters, serving as the primary network for feature extraction. It integrates a weighted bi-directional feature pyramid network that fuses multi-scale features, thereby enhancing the model's capacity to detect disease characteristics across various scales. Additionally, an efficient multi-scale attention mechanism is integrated to mitigate the influence of complex background noise in natural environments, further improving disease recognition accuracy. The experiment utilizes the AppleLeaf9 public dataset to classify healthy apple leaves and eight distinct disease types. The results indicate that, when using the augmented dataset, the improved model achieves a recognition accuracy of 95.98%, with only 1.72 M parameters, 123.16 M FLOPs, and an inference time of just 14.10 ms. Compared with eight other lightweight neural network models, including MobileNetV2,ShuffleNet_v2_1.5×, ResNet50, MobileNetV3-Large, EfficientNet-B0, MobileNetV3-Small, MobileNetV4-Conv-Small, and MobileNetV4-Conv-Medium, the improved model demonstrates superior accuracy. In particular, the proposed model achieves a recognition accuracy improvement of 0.93 percentage points compared with the baseline MobileNetV3-Small model. The optimized model introduced in this study effectively improves the accuracy in identifying diseases in apple leaves, while maintaining a low parameter count and fast inference speed, thus offering a novel approach for deploying disease recognition models on agricultural electronic devices. |
| Key words: deep learning feature fusion attention mechanism lightweight disease identification |
| DOI：10.11916/j.issn.1005-9113.24065 |
| Clc Number:TU399 |
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| Descriptions in Chinese: |
| 基于改进MobileNetV3-Small的苹果叶片病害识别模型 鄂 旭, 刘晨靠, 周 津, 宋 薇, 阎 琦, 王 嵩 （渤海大学信息科学与技术学院，辽宁 锦州，121013 ） 摘要：为提高现有网络模型对苹果叶片病害的识别准确率，本文提出一种基于改进MobileNetV3-Small网络的轻量级模型。改进模型将参数量较低的MobileNetV3-Small作为骨干网络提取特征，采用加权双向特征金字塔网络进行多尺度特征融合，增强模型对不同尺寸病害特征的识别能力，同时引入高效多尺度注意力机制以抑制自然环境下的丰富背景信息对病害识别的干扰，从而进一步提升病害识别的精度。实验采用AppleLeaf9公共数据集，对健康叶片和八种不同病害类型的苹果叶片进行识别。结果表明在增强数据集上，改进模型的识别准确率达到95.98%，模型的参数量仅为1.72 M，FLOPs为123.16 M，推理时间仅为14.10 ms，对比MobileNetV2、ShuffleNet_v2_1.5×、ResNet50、MobileNetV3-Large、EfficientNet-B0、MobileNetV3-Small、MobileNetV4-Conv-Small、MobileNetV4-Conv-Medium等8个轻量级神经网络模型，改进模型的准确率更高，相较于基准模型MobileNetV3-Small，改进模型的识别准确率提高了0.93个百分点。本文提出的改进模型在保持较低参数量和较快推理速度的前提下，提高了苹果叶片病害的识别准确率，为在农业电子设备上部署苹果叶片病害识别模型提供了新的思路。 关键词：深度学习;特征融合；注意力机制；轻量级；病害识别 |
