Improved Residual Network for Automatic Classification Grading of Lettuce Freshness

نویسندگان

چکیده

To solve the problem of low efficiency traditional lettuce freshness classification methods and sample damage, we proposed an automatic method based on improved deep residuals convolutional neural network (Im-ResNet). We built image acquisition system to obtain dataset leaves. For improving accuracy, developed for curating Then, a novel that was derived from existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): new factored extra layer, pooling fully-connected layers, random (RReLU) function. also performed corresponding experiments using Im-ResNet compared with four architectures (AlexNet, GoogleNet, VGG16 ResNet50). The experimental results showed had more significant advantages in recognition accuracy loss value networks. validation set model can reach 95.60%. Different physical chemical methods, our scheme automatically non-destructively classify lettuce.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3169159