A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Huang, Ming‐Feng; Xu, Guohai; Li, Junyu; Huang, Jianping

doi:10.3390/agriculture11121216

Cited by 16 publications

(9 citation statements)

References 51 publications

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“…With the demand for precision plant disease management, advanced tasks like localization of the disease symptom, disease spots distribution analysis, and other phenotypic feature extraction need more attention. Successful cases could be found, including disease-damaged leaf detection [ 10 ] and lesion segmentation [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning

et al. 2023

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Deep learning and computer vision have become emerging tools for diseased plant phenotyping. Most previous studies focused on image-level disease classification. In this paper, pixel-level phenotypic feature (the distribution of spot) was analyzed by deep learning. Primarily, a diseased leaf dataset was collected and the corresponding pixel-level annotation was contributed. A dataset of apple leaves samples was used for training and optimization. Another set of grape and strawberry leaf samples was used as an extra testing dataset. Then, supervised convolutional neural networks were adopted for semantic segmentation. Moreover, the possibility of weakly supervised models for disease spot segmentation was also explored. Grad-CAM combined with ResNet-50 (ResNet-CAM), and that combined with a few-shot pretrained U-Net classifier for weakly supervised leaf spot segmentation (WSLSS), was designed. They were trained using image-level annotations (healthy versus diseased) to reduce the cost of annotation work. Results showed that the supervised DeepLab achieved the best performance (IoU = 0.829) on the apple leaf dataset. The weakly supervised WSLSS achieved an IoU of 0.434. When processing the extra testing dataset, WSLSS realized the best IoU of 0.511, which was even higher than fully supervised DeepLab (IoU = 0.458). Although there was a certain gap in IoU between the supervised models and weakly supervised ones, WSLSS showed stronger generalization ability than supervised models when processing the disease types not involved in the training procedure. Furthermore, the contributed dataset in this paper could help researchers get a quick start on designing their new segmentation methods in future studies.

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Section: Introductionmentioning

confidence: 99%

Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning

et al. 2023

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“…The first stage of the proposed method is used to detect and segment vessels on each image in an independent way. We use Yolact++ [ 21 ], a recent instance segmentation algorithm designed to be faster than any previous state-of-the-art approaches and that is already being used for various kinds of segmentation tasks [ 39 , 40 ]. Its speed is achieved by breaking down instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets

Ribeiro

Damas

Bernardino

2022

Sensors

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This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which significantly improves segmentation results by exploring temporal correlations between nearby frames in video sequences. Moreover, due to the absence of maritime datasets consisting of properly labeled video sequences, we create a new dataset comprising synthetic video sequences of maritime surveillance scenarios (MarSyn). The main advantages of this approach are the possibility of generating a vast set of images and videos, being able to represent real-world scenarios without the necessity of deploying the real vehicle, and automatic labels, which eliminate human labeling errors. We train the system with the MarSyn dataset and with aerial footage from publicly available annotated maritime datasets to validate the proposed approach. We present some experimental results and compare them to other approaches, and we also illustrate the temporal stability provided by the second stage in missing frames and wrong segmentation scenarios.

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“…This method was capable of detecting millimetre-scale plant diseases through the use of deep learning and crowdsourced data. Huang et al (2021) proposed an image segmentation method based on YOLACT++ and an attention module for segmenting lesions in maize leaves under natural conditions, with the aim of improving the accuracy and real-time performance of lesion segmentation. However, these traditional CNNs still suffer from issues such as under-segmentation and low segmentation efficiency.…”

Section: Introductionmentioning

confidence: 99%

Se-Swin Unet for Image Segmentation of Major Maize Foliar Diseases

Yang,

Wang,

Zhao

et al. 2024

Eng. Agríc.

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Maize yields are important for human food security, and the issue of how to quickly and accurately segment areas of maize disease is an important one in the field of smart agriculture. To address the problem of irregular and multi-area clustering of regions of maize leaf lesions, which can lead to inaccurate segmentation, this paper proposes an improved Swin-Unet model called squeeze-and-excitation Swin-Unet (SE-Swin Unet). Our model applies Swin Transformer modules and skip connection structures for global and local learning. At each skip connection, a SENet module is incorporated to focus on global target features through channel-wise attention, with the aims of highlighting significant regions of disease on maize leaves and suppressing irrelevant background areas. The improved loss function in SE-Swin Unet is based on a combination of the binary cross entropy and Dice loss functions, which form the semantic segmentation model. Compared to other traditional convolutional neural networks on the same dataset, SE-Swin Unet achieves higher mean results for the intersection over union, accuracy, and F1-score, with values of 84.61%, 92.98%, and 89.91%, respectively. The SE-Swin Unet model proposed in this paper is therefore better able to extract information on maize leaf disease, and can provide a reference for the realisation of the complex task of corn leaf disease segmentation.

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A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++

Cited by 16 publications

References 51 publications

Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning

Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets

Se-Swin Unet for Image Segmentation of Major Maize Foliar Diseases

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