2023
DOI: 10.3389/fpls.2023.1233241
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MixSeg: a lightweight and accurate mix structure network for semantic segmentation of apple leaf disease in complex environments

Bibo Lu,
Jiangwen Lu,
Xinchao Xu
et al.

Abstract: IntroductionSemantic segmentation is effective in dealing with complex environments. However, the most popular semantic segmentation methods are usually based on a single structure, they are inefficient and inaccurate. In this work, we propose a mix structure network called MixSeg, which fully combines the advantages of convolutional neural network, Transformer, and multi-layer perception architectures.MethodsSpecifically, MixSeg is an end-to-end semantic segmentation network, consisting of an encoder and a d… Show more

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Cited by 3 publications
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“…In recent years, convolutional neural networks have made significant advancements in leaf and disease detection, which is an end-to-end learning approach ( Shi et al., 2023 ). It can automatically extract advanced image features and reduce the need for human intervention ( Lu et al., 2023a ).Convolutional neural networks demonstrate strong generalization capabilities, thus holding great potential for applications in disease detection ( Liu et al., 2017 ). Full convolutional neural networks ( Long et al., 2015 ) achieved pixel-level classification for the first time.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, convolutional neural networks have made significant advancements in leaf and disease detection, which is an end-to-end learning approach ( Shi et al., 2023 ). It can automatically extract advanced image features and reduce the need for human intervention ( Lu et al., 2023a ).Convolutional neural networks demonstrate strong generalization capabilities, thus holding great potential for applications in disease detection ( Liu et al., 2017 ). Full convolutional neural networks ( Long et al., 2015 ) achieved pixel-level classification for the first time.…”
Section: Introductionmentioning
confidence: 99%