Research on Identification of Corn Disease Occurrence Degree Based on Improved ResNeXt Network
Research on Identification of Corn Disease Occurrence Degree Based on Improved ResNeXt Network
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DOI：
10.1142/s0218001422500057
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发表时间：
2022-02-01
影响因子：
1.5
通讯作者：
Sui, Yuanyuan
中科院分区：
文献类型：
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作者：
Wang, Guowei;Wang, Jiaxin;Sui, Yuanyuan
聚合深度残差网络（ResNeXt）不仅可以在不增加参数复杂度的情况下提高精度，而且可以减少超级参数的数量。它是用于图像识别的流行卷积神经网络模型之一。玉米病害对玉米产量、品质和农民收入有很大影响。快速有效地鉴定玉米病害的严重程度对准确防治和准确用药具有重要作用。一般的ResNeXt模型在提取图像特征时斑点较大，而玉米病害的斑点较小，提取的特征不明显，影响了识别精度。因此，提出了一种改进的ResNeXt模型来识别玉米病害的发生程度。首先，根据国家标准对玉米病害程度原始数据进行分级。其次，通过数据增强对原始数据进行扩展。第三，改进了原有的ResNeXt101模型。第一层卷积核改为3个3 * 3卷积核，基数调整为64。最后对改进后的模型进行了验证。玉米病害程度的识别准确率为89.667%，比原模型提高了0.98%。通过对实际采集的276幅玉米病害图像进行测试，识别准确率达到90.22%。因此，该方法用于玉米病害程度诊断是可行的，可为精准防控提供重要依据。
Aggregate depth residual network (ResNeXt) can not only improve the accuracy without increasing the parameter complexity, but also reduce the number of super parameters. It is one of the popular convolutional neural network models for image recognition. Maize diseases have a great impact on maize yield, quality and farmers' income. Rapid and effective identification of the severity of maize diseases plays an important role in accurate control and accurate drug use. The general ResNeXt model has large spots in extracting image features, but the spots of corn diseases are small and the extracted features are not obvious, which affects the recognition accuracy. Therefore, an improved ResNeXt model is proposed to recognize the occurrence degree of corn diseases. First, the original data of maize disease degree are classified according to national standards. Second, the original data are extended through data enhancement. Third, the original ResNeXt101 model is improved. The first layer convolution kernel is changed to three 3 * 3 convolution kernels, and the cardinality is adjusted to 64. Finally, the improved model is verified. The recognition accuracy of corn disease degree is 89.667%, which is 0.98% higher than the original model. Through testing on 276 actually collected corn disease images, the recognition accuracy is 90.22%. Therefore, this method is feasible for the diagnosis of corn disease degree and can provide an important basis for accurate prevention and control.