MixSeg: a lightweight and accurate mix structure network for semantic segmentation of apple leaf disease in complex environments

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 decoder. In the encoder, the Mix Transformer is designed to model globally and inject local bias into the model with less computational cost. The position indexer is developed to dynamically index absolute position information on the feature map. The local optimization module is designed to optimize the segmentation effect of the model on local edges and details. In the decoder, shallow and deep features are fused to output accurate segmentation results.ResultsTaking the apple leaf disease segmentation task in the real scene as an example, the segmentation effect of the MixSeg is verified. The experimental results show that MixSeg has the best segmentation effect and the lowest parameters and floating point operations compared with the mainstream semantic segmentation methods on small datasets. On apple alternaria blotch and apple grey spot leaf image datasets, the most lightweight MixSeg-T achieves 98.22%, 98.09% intersection over union for leaf segmentation and 87.40%, 86.20% intersection over union for disease segmentation.DiscussionThus, the performance of MixSeg demonstrates that it can provide a more efficient and stable method for accurate segmentation of leaves and diseases in complex environments.

RAAWC-UNet: an apple leaf and disease segmentation method based on residual attention and atrous spatial pyramid pooling improved UNet with weight compression loss

“…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.…”