Image Segmentation Method for Sweetgum Leaf Spots Based on an Improved DeeplabV3+ Network

Cai, Maodong; Yi, Xiaomei; Wang, Guoying; Mo, Lan; Wu, Peng; Mwanza, Christine; Kapula, Kasanda Ernest

doi:10.3390/f13122095

Cited by 13 publications

(7 citation statements)

References 24 publications

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“…In this experiment, the evaluation index defined in Evaluation indicators was used to test the performance of the MC-UNet model with that of UNet, UNet++, TransUNet [ 51 ], Attention-UNet [ 52 ], SegNet [ 53 ], DeepLabV3 [ 54 ], PSPNet [ 55 ], M DeeplabV3+ [ 56 ] and M UNet [ 57 ]. Accuracy distribution of each model is plotted using Boxplot, as shown in Table 9 and Fig.…”

Section: Resultsmentioning

confidence: 99%

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

Deng

Zhou

et al. 2023

Plant Phenomics

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Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

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Section: Resultsmentioning

confidence: 99%

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

Deng

Zhou

et al. 2023

Plant Phenomics

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“…Wang [43] successfully enhanced the discriminative capability of the ecological environmental elements in the Yangtze River source region by incorporating CBAM into the ASPP layer. Cai [50] achieved increased precision in the segmentation of camphor leaf spots by adding CBAM to the upsampling layer. To investigate the impact of CBAM modules in the field of crop recognition, we introduced CBAM separately into the upsampling layer and ASPP layer.…”

Section: Advantages Of the Algorithm In This Papermentioning

confidence: 99%

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

Chang,

Li,

Chen

et al. 2023

Remote Sensing

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Remote sensing technology has become a popular tool for crop classification, but it faces challenges in accurately identifying crops in areas with fragmented land plots and complex planting structures. To address this issue, we propose an improved method for crop identification in high-resolution remote sensing images, achieved by modifying the DeepLab V3+ semantic segmentation network. In this paper, the typical crop area in the Jianghuai watershed is taken as the experimental area, and Gaofen-2 satellite images with high spatial resolutions are used as the data source. Based on the original DeepLab V3+ model, CI and OSAVI vegetation indices are added to the input layers, and MobileNet V2 is used as the backbone network. Meanwhile, the upper sampling layer of the network is added, and the attention mechanism is added to the ASPP and the upper sampling layers. The accuracy verification of the identification results shows that the MIoU and PA of this model in the test set reach 85.63% and 95.30%, the IoU and F1_Score of wheat are 93.76% and 96.78%, and the IoU and F1_Score of rape are 74.24% and 85.51%, respectively. The identification accuracy of this model is significantly better than that of the original DeepLab V3+ model and other related models. The proposed method in this paper can accurately extract the distribution information of wheat and rape from high-resolution remote sensing images. This provides a new technical approach for the application of high-resolution remote sensing images in identifying wheat and rape.

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“…Kuznetsova et al [15] used Yolov3 as the detection system for a fruit-picking robot and achieved the results, with an average apple detection time of 19 ms, 7.8% misidentified apples, and 9.2% unidentified apples. Cai et al [16] proposed a method for segmenting spotted fragrant tree leaf images using a modified DeepLabv3+ network. The model exhibited excellent segmentation performance for different levels of scattered spot segmentation that could quickly assess the disease condition, and thus contributed to garden conservation.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Log Diameter Measurement Using Wood Images Based on Yolov3 and DeepLabv3+

Lu,

Yao,

Lyu

et al. 2024

Forests

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Wood volume is an important indicator in timber trading, and log diameter is one of the primary parameters used to calculate wood volume. Currently, the most common methods for measuring log diameters are manual measurement or visual estimation by log scalers, which are laborious, time consuming, costly, and error prone owing to the irregular placement of logs and large numbers of roots. Additionally, this approach can easily lead to misrepresentation of data for profit. This study proposes a model for automatic log diameter measurement that is based on deep learning and uses images to address the existing problems. The specific measures to improve the performance and accuracy of log-diameter detection are as follows: (1) A dual network model is constructed combining the Yolov3 algorithm and DeepLabv3+ architecture to adapt to different log-end color states that considers the complexity of log-end faces. (2) AprilTag vision library is added to estimate the camera position during image acquisition to achieve real-time adjustment of the shooting angle and reduce the effect of log-image deformation on the results. (3) The backbone network is replaced with a MobileNetv2 convolutional neural network to migrate the model to mobile devices, which reduces the number of network parameters while maintaining detection accuracy. The training results show that the mean average precision of log-diameter detection reaches 97.28% and the mean intersection over union (mIoU) of log segmentation reaches 92.22%. Comparisons with other measurement models demonstrate that the proposed model is accurate and stable in measuring log diameter under different environments and lighting conditions, with an average accuracy of 96.26%. In the forestry test, the measurement errors for the volume of an entire truckload of logs and a single log diameter are 1.20% and 0.73%, respectively, which are less than the corresponding error requirements specified in the industry standards. These results indicate that the proposed method can provide a viable and cost-effective solution for measuring log diameters and offering the potential to improve the efficiency of log measurement and promote fair trade practices in the lumber industry.

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Image Segmentation Method for Sweetgum Leaf Spots Based on an Improved DeeplabV3+ Network

Cited by 13 publications

References 24 publications

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

Crop Type Identification Using High-Resolution Remote Sensing Images Based on an Improved DeepLabV3+ Network

A Deep Learning Method for Log Diameter Measurement Using Wood Images Based on Yolov3 and DeepLabv3+

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