Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations
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摘要:
目的 针对芒果叶片病虫害缺少数据集和识别准确率低的问题,筛选构建芒果叶片病虫害分类模型,以提高芒果叶病虫害分类准确率。 方法 提出使用去噪扩散模型进行病虫害数据增强,同时联合SKNet与MobilenetV3模型的芒果叶片病虫害分类方法。首先使用去噪扩散模型对数据集进行扩充,再采用多尺度结构相似性指标对生成的病虫害图像与拍摄的病虫害图像之间的相似程度进行评估,接着对DDIM与DCGAN网络训练和生成效果进行比对。在MobilenetV3模型中,将SE注意力模块替换为SKNet模块进行构建网络模型。 结果 使用DDIM生成的所有类型的病虫害图像与拍摄的病虫害图像的MS-SSIM指标均大于0.63,且都高于DCGAN。相较于其他注意力模块,联合SKNet与MobilenetV3的分类效果最佳,在98%以上。对添加CA、CBAM、ECA注意力模块进行平滑类激活图可视化,对比其他注意力模块,使用SKNet注意力分布区域更为集中在病虫害叶片上。 结论 该方法在病虫害叶片检测上具有良好的应用前景,能提升病虫害识别效率与精度,减少检测成本,同时可应用于移动式或者嵌入式设备。 -
关键词:
- 芒果叶片 /
- 扩散概率模型 /
- Mobilenet /
- Selective Kernel Networks
Abstract:Objective Leaf diseases and infestations on mango trees were classified for database establishment and precision identification by combining the Mobilenet V3 model with Selective Kernel Network (SKNet). Method To improve the accuracy of disease and infestation classification on mango plants, data augmentation was firstly conducted. A denoising diffusion model was applied to expand the dataset followed by using a multi-scale structural similarity index to examine the similarity between the virtually generated and the camera-captured images of the diseases or infestations. Then, the training and generation effects of DDIM and DCGAN networks were compared. In the Mobilenet V3 model, the SE attention module was replaced with SKNet to construct the final platform. Results The MS-SSIM index of all types of DDIM images was greater than 0.63, which was higher than that of DCGAN. The classification accuracy of 98% delivered by merging SKNet with Mobilenet V3 was the best performance. Furthermore, combination of the two programs afforded more focus on the diseased leaves than did other smooth grade activation visualization by adding CA, CBAM, or ECA. Conclusion The newly developed classification method by integrating SKNet and Mobilenet V3 performed satisfactorily in distinguishing various diseased or infested mango leaves. The application not only significantly improved the efficiency and accuracy of disease identification but also reduced the epidemic monitoring costs by easily incorporating it with mobile or embedded devices. -
Key words:
- Mango leaf /
- diffusion probability model /
- Mobilenet /
- Selective Kernel Networks
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表 1 MobilenetV3模型结构
Table 1. Structure of Mobilenet V3
输入尺寸
Input shape操作算子
Operation扩展尺寸
Expand size输出通道
Output channelSE模块
SE module激活函数
Activation function步长
Stride2242 conv2d,3×3 — 16 — HS 2 1122×16 bneck,3×3 16 16 √ RE 2 562×16 bneck,3×3 72 24 — RE 2 282×24 bneck,3×3 88 24 — RE 1 282×24 bneck,5×5 96 40 √ HS 2 142×40 bneck,5×5 240 40 √ HS 1 142×40 bneck,5×5 240 40 √ HS 1 142×40 bneck,5×5 120 48 √ HS 1 142×48 bneck,5×5 144 48 √ HS 1 142×48 bneck,5×5 288 96 √ HS 2 72×96 bneck,5×5 576 96 √ HS 1 72×96 bneck,5×5 576 96 √ HS 1 72×96 conv2d,1×1 — 576 √ HS 1 72×576 pool,7×7 — — — — 1 12×576 conv2d1×1,NBN — 1280 — HS 1 12× 1024 conv2d1×1,NBN — 7 — — 1 conv2d—二维卷积;bneck—瓶颈模块;pool—池化层;NBN—不使用批量归一化;HS—硬切线激活函数;RE—修正线性单元;√—使用SE模块。
conv2d: 2D convolution; bneck: bottleneck module; pool: pooling layer; NBN: no batch normalization; HS: hard swish activation function; RE: ReLU (rectified linear unit); √: SE module applied.表 2 图片生成前后数据量比较
Table 2. Data volumes before and after image generation
类型
Type原始图数量
Original dataset增强后图像数量
Augmented dataset炭疽病
Colletotrichum gloeosporioides320 1800 细菌性角斑病
Xanthomonas campestris pv. mangiferaeindicae324 1800 切叶象甲 Myllocerus viridanus 320 1800 枯萎病 Fusarium oxysporum 320 1800 瘿蚊 Erosomyia mangiferae 544 1800 白粉病 Oidium mangiferae 320 1800 煤污病 Capnodium mangiferae 320 1800 表 3 DDIM与DCGAN网络模型训练指标对比
Table 3. Network model training metrics of DDIM and DCGAN
指标 Index DDIM DCGAN 模型大小 Model size/MB 117 89 训练时间 Training time /h 48 36 收敛速度(训练轮次) Convergence speed (epochs) 19 57 训练中损失函数的标准偏差 Standard deviation 0.05 0.15 总耗时 Total time/s 392.6 457.2 表 4 生成的病虫害图像与拍摄的病虫害图像MS-SSIM值
Table 4. MS-SSIM values on virtually generated and camera-captured images of diseased leaves
图像类型 Image type DDIM DCGAN 炭疽病 Colletotrichum gloeosporioides 0.6312 0.5992 细菌性角斑病
Xanthomonas campestris pv. mangiferaeindicae0.7298 0.6912 切叶象甲 Myllocerus viridanus 0.6754 0.6413 枯萎病 Fusarium oxysporum 0.7123 0.6805 瘿蚊 Erosomyia mangiferae 0.7459 0.6990 白粉病 Oidium mangiferae 0.7211 0.6853 煤污病 Capnodium mangiferae 0.6671 0.6396 表 5 引入注意力模块的MobilenetV3模型试验对比
Table 5. Experimental results on Mobilenet V3 model introduced with attention modules
算法
Algorithm准确率
Accuracy/%参数量
Params/M乘加运算数
MACs/GMobilenetV3(SE) 97.24 2.54 0.06 MobilenetV3+CA 95.75 2.18 0.06 MobilenetV3+CBAM 97.69 2.59 0.09 MobilenetV3+ECA 97.51 2.08 0.06 MobilenetV3+SKNet 98.21 2.58 0.06 -
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