Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3 +

Wu, Mengliu; Li, Jinsong; Shen, Yang; Zheng, Lihua; Liu, Xinliang; Wang, Minjuan

doi:10.1007/s11694-022-01660-3

Cited by 8 publications

(1 citation statement)

References 58 publications

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“…Yan et al [14] improved DeepLabv3+ and proposed a method for tea segmentation and picking point localization based on lightweight convolutional neural networks to address the issue of tea bud picking points in real environments, achieving a Mean Intersection over Union (MIoU) of 91.85%. Zhang et al [15] enhanced DeepLabv3+ to perform high-precision and rapid lettuce segmentation in complex background and lighting conditions. Yu et al [16] utilized the Swin transformer as a feature extraction network and incorporated a convolution block attention module into DeepLabv3+ to obtain the Swin-DeepLabv3+ model for weed segmentation in soybean fields, achieving an MIoU of 91.53%.…”

Section: Introductionmentioning

confidence: 99%

Method for Segmentation of Banana Crown Based on Improved DeepLabv3+

Duan

Yang

et al. 2023

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As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment.

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Section: Introductionmentioning

confidence: 99%