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Maps (second row), predicted segFigure 7. Leaf photos (1st row), ground truth segmentation maps (second row), predicted segmenmentation feature maps just after coaching complete network (last row). tation function maps after coaching entire network (final row).3.four. Overall 5-Methyl-2-thiophenecarboxaldehyde Biological Activity performance Comparison three.four. Efficiency Comparison three.4.1. Leaf Disease Identification three.four.1. Leaf Illness Identification Table 11presents the CRC results for the proposed method and traditional stateofTable presents the CRC final results for the proposed approach and traditional stateoftheart solutions. In Table 1, all networks, like VGG, ResNet, FPN, AGN, SqueezeNet, theart procedures. In Table 1, all networks, such as VGG, ResNet, FPN, AGN, and PVT, had been initialized with all the pretrained parameters with an ImageNet dataset, and SqueezeNet, and PVT, had been initialized with all the pretrained parameters with an ImageNet TL was applied to each networkeach networkapple a new apple leaf dataset.PVT, FPN, dataset, and TL was applied to having a new with leaf dataset. Except for Except for and AGN, library functions of MATLAB (2021a) have been usedwere utilized to conventional PVT, FPN, and AGN, library functions of MATLAB (2021a) to train the train the conclassification models: VGG, ResNet, and SqueezeNet. The optimizer employed was stochastic ventional classification models: VGG, ResNet, and SqueezeNet. The optimizer utilised was gradient descent (SGD) [45] (SGD) [45] with momentum. The epoch quantity was 30, and stochastic gradient descent with momentum. The epoch number was 30, plus the batch size batch size was The studying learning price was 0.001, as well as the momentum term was set the was set to 10. set to ten. The rate was 0.001, as well as the momentum term was set to 0.9. The0.9. The regularization term was and its weight was set to 0.0001.to 0.0001. For additional to regularization term was two norm, norm, and its weight was set For a lot more detailed parameter settings, 2-Hydroxychalcone Protocol please refer to therefer towards the author’s source code. For opensource detailed parameter settings, please author’s source code. For the PVT, the the PVT, the code supplied by the author in [24] was applied with wasdefault setting. default setting. were opensource code supplied by the author in [24] the made use of with all the AGN and FPN AGN implemented using MATLAB’s layer functions.layer functions.exact same education parameters and FPN had been implemented employing MATLAB’s As a result, the Hence, the identical trainwereparameters have been utilised for the traditional and proposed models, except for PVT. ing made use of for the traditional and proposed models, except for PVT.Table 1. Performance evaluation for leaf disease identification. Table 1. Efficiency evaluation for leaf illness identification. Solutions Methods VGG16 [13] VGG16 [13] ResNet50 ResNet50 [14] [14] CRC CRC 90.19 90.19 87.87 87.69 88.58 92.24 93.70 96.Focus Gated Network Interest Gated Network (AGN) [22] (AGN) [22] Feature Pyramid Network Feature Pyramid Network (FPN) [23] (FPN) [23] SqueezeNet [43] SqueezeNet [43] Pyramid Vision Transformer (PVT) [24] Pyramid Vision Transformer (PVT) [24] Proposed LSANetProposed LSANet87.87 87.69 88.58 92.24 93.70 96.In Figure three, in the event the ROIaware FES and ROIaware feature fusion are excluded from the LSANet, 3, if proposed architecture becomes identical for the standard VGG the In Figure the the ROIaware FES and ROIaware function fusion are excluded from network. For that reason, it really should be checked regardless of whether CRC might be enhanced with VGG network. LSANet, the proposed architecture becomes identical for the c.

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