农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差...农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差神经网络(Residual Network,ResNet)50、视觉几何群网络(Visual Geometry Group Network,VGG)16以及微调预训练模型VGG16共4种网络模型二分类农田害虫图片集。由于样本数据量较少,为防止出现过拟合,使用了数据增强技术,即通过现有训练图片生成更多的训练图片,从而提高泛化能力。实验表明,4种网络模型的准确率分别为88.63%、91.73%、86.49%和90.13%,在农田害虫识别中均具有较好的实际应用效果。展开更多
The changes in cotton leaf characteristics are closely related to the cotton spider mites’damage level.Extracting the distinguishable features of cotton leaves is an effective method to identify the level.However,it ...The changes in cotton leaf characteristics are closely related to the cotton spider mites’damage level.Extracting the distinguishable features of cotton leaves is an effective method to identify the level.However,it faces enormous challenges for the classification due to various factors,such as illumination intensity,background complexity,shooting angle and so on.A recognition model is proposed,which is trained through transfer learning with the two-stage learning rate from 0.01 to 0.001 based on MobileNetV1.The experiments demonstrate that the deep learning model attains the accuracy of 92.29%for the training set and 91.88%for the test set of the mixed data.For testifying the effectiveness of the two-stage training method,the models are trained with the two public datasets,CIFAR-10 and Flowers,and attain the accuracy of 95.46%and 95.57%for the test sets,respectively.The average recognition time for a single cotton leaf image is about 0.015 s.Furthermore,the mobile terminal application is developed with the model embedded,to realize the real-time recognition for cotton spider mites’damage level in the field.展开更多
文摘农田害虫降低了农作物的产量和质量,如何有效区分和治理农田害虫成为首要解决的问题。文章紧抓农田环境需求和农民对农作物的产量需求不匹配的痛点,基于卷积神经网络技术识别农田害虫,为农业提供有效的识别方式。采用MobileNetV1、残差神经网络(Residual Network,ResNet)50、视觉几何群网络(Visual Geometry Group Network,VGG)16以及微调预训练模型VGG16共4种网络模型二分类农田害虫图片集。由于样本数据量较少,为防止出现过拟合,使用了数据增强技术,即通过现有训练图片生成更多的训练图片,从而提高泛化能力。实验表明,4种网络模型的准确率分别为88.63%、91.73%、86.49%和90.13%,在农田害虫识别中均具有较好的实际应用效果。
基金Thanks for the support of National Key Research and Development Program of China(No.2016YFB0501805)2017 New Mode Application Project of Intelligent Manufacturing(New Mode Application of Remote Operation and Maintenance Service for Modern Agricultural Machinery Equipment).
文摘The changes in cotton leaf characteristics are closely related to the cotton spider mites’damage level.Extracting the distinguishable features of cotton leaves is an effective method to identify the level.However,it faces enormous challenges for the classification due to various factors,such as illumination intensity,background complexity,shooting angle and so on.A recognition model is proposed,which is trained through transfer learning with the two-stage learning rate from 0.01 to 0.001 based on MobileNetV1.The experiments demonstrate that the deep learning model attains the accuracy of 92.29%for the training set and 91.88%for the test set of the mixed data.For testifying the effectiveness of the two-stage training method,the models are trained with the two public datasets,CIFAR-10 and Flowers,and attain the accuracy of 95.46%and 95.57%for the test sets,respectively.The average recognition time for a single cotton leaf image is about 0.015 s.Furthermore,the mobile terminal application is developed with the model embedded,to realize the real-time recognition for cotton spider mites’damage level in the field.
