An improved ResNet network based healing judgment algorithm for grafted seedlings
Pages 399 - 407
Abstract
At present, the judgment of the healing survival of grafted seedlings mainly depends on the artificial observation of the appearance of scion true leaves, which leads to the delay in the judgment of the healing status, and reduces the utilization rate of the healing device in the large-scale production of grafted seedlings. Therefore, this paper proposed a classification model of the healing state of grafted seedlings based on convolutional neural network. In this paper, based on ResNet-50 network, deep detachable convolution is introduced to greatly reduce the number of network parameters by replacing ordinary convolution in residual network. At the same time, multi-scale deep detachable convolution kernel is constructed by referring to multi-scale fusion characteristics to enhance the adaptability of network to scale.SE attention mechanism module is introduced to improve the extraction of image features by network. The model parameters were optimized by transfer learning method to further improve the model accuracy. Experimental results show that the improved model reduces the number of parameters by nearly 30%, and the average accuracy on the self-built data set is 96.98%. Compared with the original network, the accuracy is improved by 2.9%, realizing the double optimization improvement of the number of parameters and accuracy of the model.
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Index Terms
- An improved ResNet network based healing judgment algorithm for grafted seedlings
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Published In
October 2021
593 pages
ISBN:9781450395588
DOI:10.1145/3500931
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Association for Computing Machinery
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Published: 22 December 2021
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ISAIMS 2021
ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences
October 29 - 31, 2021
Beijing, China
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Overall Acceptance Rate 53 of 112 submissions, 47%
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