School of Computer Science and Technology, Anhui University of Technology, Ma'anshan 243032, China.
Institute of Agricultural Economy and Information, Anhui Academy of Agricultural Sciences, Hefei 230031, China.
Sensors (Basel). 2022 Jul 25;22(15):5550. doi: 10.3390/s22155550.
Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in time, and reduce crop losses. Inspired by the application of convolutional neural networks in image identification, we propose a lightweight crop disease image identification model based on attentional feature fusion named DSGIResNet_AFF, which introduces self-built lightweight residual blocks, inverted residuals blocks, and attentional feature fusion modules on the basis of ResNet18. We apply the model to the identification of rice and corn diseases, and the results show the effectiveness of the model on the real dataset. Additionally, the model is compared with other convolutional neural networks (AlexNet, VGG16, ShuffleNetV2, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large), and the experimental results show that the accuracy, sensitivity, F1-score, AUC of the proposed model DSGIResNet_AFF are 98.30%, 98.23%, 98.24%, 99.97%, respectively, which are better than other network models, while the complexity of the model is significantly reduced (compared with the basic model ResNet18, the number of parameters is reduced by 94.10%, and the floating point of operations(FLOPs) is reduced by 86.13%). The network model DSGIResNet_AFF can be applied to mobile devices and become a useful tool for identifying crop diseases.
作物病害是影响作物产量和品质的重要因素之一，也是农业领域的重要研究目标。为了快速准确地识别作物病害，帮助农民及时控制作物病害，减少作物损失。受卷积神经网络在图像识别中的应用启发，我们提出了一种基于注意力特征融合的轻量级作物病害图像识别模型，命名为 DSGIResNet_AFF，该模型在 ResNet18 的基础上引入了自建的轻量级残差块、倒置残差块和注意力特征融合模块。我们将该模型应用于水稻和玉米病害的识别，结果表明该模型在真实数据集上的有效性。此外，该模型与其他卷积神经网络（AlexNet、VGG16、ShuffleNetV2、MobileNetV2、MobileNetV3-Small 和 MobileNetV3-Large）进行了比较，实验结果表明，所提出的模型 DSGIResNet_AFF 的准确率、灵敏度、F1 分数和 AUC 分别为 98.30%、98.23%、98.24%和 99.97%，优于其他网络模型，同时模型的复杂度显著降低（与基础模型 ResNet18 相比，参数量减少了 94.10%，浮点运算次数减少了 86.13%）。该网络模型 DSGIResNet_AFF 可应用于移动设备，成为识别作物病害的有用工具。
