Zhu Xianyu, Li Jinjiang, Jia Runchang, Liu Bin, Yao Zhuohan, Yuan Aihong, Huo Yingqiu, Zhang Haixi
IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar-Apr;20(2):1156-1169. doi: 10.1109/TCBB.2022.3191854. Epub 2023 Apr 3.
Aphids, brown spots, mosaics, rusts, powdery mildew and Alternaria blotches are common types of early apple leaf pests and diseases that severely affect the yield and quality of apples. Recently, deep learning has been regarded as the best classification model for apple leaf pests and diseases. However, these models with large parameters have difficulty providing an accurate and fast diagnosis of apple leaf pests and diseases on mobile terminals. This paper proposes a novel and real-time early apple leaf disease recognition model. AD Convolution is firstly utilized to replace standard convolution to make smaller number of parameters and calculations. Meanwhile, a LAD-Inception is built to enhance the ability of extracting multiscale features of different sizes of disease spots. Finally, the LAD-Net model is built by the LR-CBAM and the LAD-Inception modules, replacing a full connection with global average pooling to further reduce parameters. The results show that the LAD-Net, with a size of only 1.25MB, can achieve a recognition performance of 98.58%. Additionally, it is only delayed by 15.2ms on HUAWEI P40 and by 100.1ms on Jetson Nano, illustrating that the LAD-Net can accurately recognize early apple leaf pests and diseases on mobile devices in real-time, providing portable technical support.
蚜虫、褐斑病、花叶病、锈病、白粉病和链格孢叶斑病是常见的苹果早期叶部病虫害类型，严重影响苹果的产量和品质。近年来，深度学习被视为苹果叶部病虫害的最佳分类模型。然而，这些参数庞大的模型在移动终端上难以对苹果叶部病虫害进行准确快速的诊断。本文提出了一种新颖的实时苹果早期叶部病害识别模型。首先利用自适应卷积（AD Convolution）取代标准卷积，以减少参数数量和计算量。同时，构建了局部自适应深度卷积网络（LAD-Inception）来增强提取不同大小病斑多尺度特征的能力。最后，通过轻量化残差注意力模块（LR-CBAM）和LAD-Inception模块构建了LAD-Net模型，用全局平均池化取代全连接层以进一步减少参数。结果表明，LAD-Net模型大小仅为1.25MB，识别准确率可达98.58%。此外，在华为P40上延迟仅为15.2ms，在英伟达Jetson Nano上延迟为100.1ms，这表明LAD-Net能够在移动设备上实时准确地识别苹果早期叶部病虫害，提供了便捷的技术支持。
