基于迁移学习的苹果落叶病识别与应用  

Recognition and application of apple defoliation disease based on transfer learning

作　　者：郭惠萍[1] 曹亚州 王晨思 荣麟瑞 李怡 王霆伟 杨福增[1] GUO Huiping;CAO Yazhou;WANG Chensi;RONG Linrui;LI Yi;WANG Tingwei;YANG Fuzeng(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling 712100,China;Xi’an BYD Automotive Engineering Research Institute,Xi’an 710000,China)

机构地区：[1]西北农林科技大学机械与电子工程学院,杨凌712100 [2]西安比亚迪汽车工程研究院,西安710000

出　　处：《农业工程学报》2024年第3期184-192,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：陕西省科技重大专项(2020zdzx03-04-01,2020zdzx03-04-02);陕西省技术创新引导专项(2021QFY08-05)。

摘　　要：为解决现有卷积神经网络苹果叶片病害识别模型泛化能力弱,模型体积较大等问题,该研究提出一种基于改进MobileNetV3苹果落叶病识别模型。以健康叶片和常见苹果落叶病为研究对象,包括斑点落叶病、灰斑病、褐斑病、锈病4种,每种病害2级,共9类特征,通过改进网络的注意力模块、全连接层及算子,结合迁移学习的训练方式,构建苹果落叶病识别模型。在扩充前后的数据集上对比不同的学习方式、学习率和注意力模块等对模型的影响,验证模型的识别性能。试验结果表明:采用迁移学习的方式,在训练50轮达曲线收敛,比全新学习的准确率增加6.74~10.79个百分点;使用引入的ET(efficient channel attention-tanh)注意力模块,网络损失曲线更加平滑,模型的参数量更少,模型体积减小了48%,提高了模型的泛化能力;在扩充数据集上,学习率为0.000 1时,结合迁移学习的训练方式,改进MobileNetV3(ET3-MobileNetV3)苹果落叶病识别模型,平均准确率能达到95.62%,模型体积6.29 MB。将模型部署到喷药设备上,可实现基于苹果叶片病害识别的变量喷施,该研究可为苹果叶片病害的检测与果园的现代化管理提供参考。Convolutional Neural Network(CNN)can be applied to recognize the leaf disease of apples in agricultural production,due to the reduced size of the model and the high generalization.In this study,the recognition model was proposed for the apple defoliation disease using an improved MobileNetV3(ET3-MobileNetV3)network structure.According to the disease features,the images were divided into five types:altermaria boltch,rust,grey spot,brown spot and health.The images of apple defoliation disease were collected from the standard dataset produced by the Luochuan Apple Experimental Station of Northwest A&F University.The orchard dataset was collected from Huicheng Apple Farm in Yangling City.The final datasets of apple defoliation diseases were a total of 21950 images,including 19819 in the training set and 2131 in the test set.The recognition model of defoliation disease was constructed to improve the attention module.Efficient Channel Attention(ECA)was replaced by Squeeze and Excite(SE),while the Tanh function was used to replace the Sigmoid function.Then,the full connection layer was improved to replace the original Hard-Swish(HS)activation function with the ReLU6 activation function.At the same time,the Dropout layer and the improved Bottleneck operator were introduced to enhance the calculation speed.Finally,transfer learning was utilized to transfer the pre-trained weights into the recognition task in the training.Among them,the pre-trained weights were obtained to train the model on the ImageNet dataset.The generalization was improved on the small sample learning,whereas,there was a decrease in the data demand of the target task,indicating the better performance of the model.The performance of recognition was verified by training the model on datasets before and after expansion,transfer learning and new learning,different learning rates and attention mechanisms.The learning rates included 0.01,0.001,and 0.0001.Attention modules included SE,ECA and Efficient Channel Attention-Tanh(ET).The experimental results showe

关 键 词：病害 图像识别 苹果落叶病 ET注意力模块 改进MobileNetV3 迁移学习 

分 类 号：S23[农业科学—农业机械化工程]