A tomato disease identification method based on leaf image automatic labeling algorithm and improved <scp>YOLOv5</scp> model

Jing, Jiaping; Li, Shufei; Qiao, Chunming; Li, Kaiyu; Zhu, Xun; Zhang, Lingxian

doi:10.1002/jsfa.12793

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…proposed the use of the squeeze and excitation module to improve YOLOv5 detection performance on small disease objects affected by tomato virus disease. Jing et al 27 . introduced the CBAM attention mechanism 28 .…”

Section: Introductionmentioning

confidence: 99%

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Wang,

Liu

2024

Sci Rep

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This study introduces YOLOv8n-vegetable, a model designed to address challenges related to imprecise detection of vegetable diseases in greenhouse plant environment using existing network models. The model incorporates several improvements and optimizations to enhance its effectiveness. Firstly, a novel C2fGhost module replaces partial C2f. with GhostConv based on Ghost lightweight convolution, reducing the model’s parameters and improving detection performance. Second, the Occlusion Perception Attention Module (OAM) is integrated into the Neck section to better preserve feature information after fusion, enhancing vegetable disease detection in greenhouse settings. To address challenges associated with detecting small-sized objects and the depletion of semantic knowledge due to varying scales, an additional layer for detecting small-sized objects is included. This layer improves the amalgamation of extensive and basic semantic knowledge, thereby enhancing overall detection accuracy. Finally, the HIoU boundary loss function is introduced, leading to improved convergence speed and regression accuracy. These improvement strategies were validated through experiments using a self-built vegetable disease detection dataset in a greenhouse environment. Multiple experimental comparisons have demonstrated the model's effectiveness, achieving the objectives of improving detection speed while maintaining accuracy and real-time detection capability. According to experimental findings, the enhanced model exhibited a 6.46% rise in mean average precision (mAP) over the original model on the self-built vegetable disease detection dataset under greenhouse conditions. Additionally, the parameter quantity and model size decreased by 0.16G and 0.21 MB, respectively. The proposed model demonstrates significant advancements over the original algorithm and exhibits strong competitiveness when compared with other advanced object detection models. The lightweight and fast detection of vegetable diseases offered by the proposed model presents promising applications in vegetable disease detection tasks.

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Section: Introductionmentioning

confidence: 99%

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Wang,

Liu

2024

Sci Rep

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“…In 2021, China’s tomato production reached 67.63 million tons, accounting for 35% of the world’s total output, which is a very popular garden plant ( Bhatkar et al., 2021 ; Jing et al., 2023 ; Liu et al., 2023 ). However, plant disease resistance needs to be faced by all crops in large-scale production, and various diseases can damage crops in any given growing season, resulting in reduced yield or lower quality.…”

Section: Introductionmentioning

confidence: 99%

A ResNet50-DPA model for tomato leaf disease identification

Liang,

Jiang

2023

Front. Plant Sci.

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Tomato leaf disease identification is difficult owing to the variety of diseases and complex causes, for which the method based on the convolutional neural network is effective. While it is challenging to capture key features or tends to lose a large number of features when extracting image features by applying this method, resulting in low accuracy of disease identification. Therefore, the ResNet50-DPA model is proposed to identify tomato leaf diseases in the paper. Firstly, an improved ResNet50 is included in the model, which replaces the first layer of convolution in the basic ResNet50 model with the cascaded atrous convolution, facilitating to obtaining of leaf features with different scales. Secondly, in the model, a dual-path attention (DPA) mechanism is proposed to search for key features, where the stochastic pooling is employed to eliminate the influence of non-maximum values, and two convolutions with one dimension are introduced to replace the MLP layer for effectively reducing the damage to leaf information. In addition, to quickly and accurately identify the type of leaf disease, the DPA module is incorporated into the residual module of the improved ResNet50 to obtain an enhanced tomato leaf feature map, which helps to reduce economic losses. Finally, the visualization results of Grad-CAM are presented to show that the ResNet50-DPA model proposed can identify diseases more accurately and improve the interpretability of the model, meeting the need for precise identification of tomato leaf diseases.

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“…Albattah et al (2021) proposed a framework for automatic detection and classification of plant diseases based on DenseNET-77, which cannot be deployed on mobile devices. Jing et al (2023)improved upon YOLOv5 by incorporating the Convolutional Block Attention Module (CBAM) and replacing the original FPN with BiFPN, achieving detection of 9 types of tomato diseases and healthy leaves with a 96.4% accuracy rate while also obtaining good tomato disease image annotation results. However, data images in controlled experimental environments often have simple backgrounds and sufficient lighting.…”

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confidence: 99%

Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion

Wang,

Zhang,

Tian

2024

Front. Plant Sci.

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When detecting tomato leaf diseases in natural environments, factors such as changes in lighting, occlusion, and the small size of leaf lesions pose challenges to detection accuracy. Therefore, this study proposes a tomato leaf disease detection method based on attention mechanisms and multi-scale feature fusion. Firstly, the Convolutional Block Attention Module (CBAM) is introduced into the backbone feature extraction network to enhance the ability to extract lesion features and suppress the effects of environmental interference. Secondly, shallow feature maps are introduced into the re-parameterized generalized feature pyramid network (RepGFPN), constructing a new multi-scale re-parameterized generalized feature fusion module (BiRepGFPN) to enhance feature fusion expression and improve the localization ability for small lesion features. Finally, the BiRepGFPN replaces the Path Aggregation Feature Pyramid Network (PAFPN) in the YOLOv6 model to achieve effective fusion of deep semantic and shallow spatial information. Experimental results indicate that, when evaluated on the publicly available PlantDoc dataset, the model’s mean average precision (mAP) showed improvements of 7.7%, 11.8%, 3.4%, 5.7%, 4.3%, and 2.6% compared to YOLOX, YOLOv5, YOLOv6, YOLOv6-s, YOLOv7, and YOLOv8, respectively. When evaluated on the tomato leaf disease dataset, the model demonstrated a precision of 92.9%, a recall rate of 95.2%, an F1 score of 94.0%, and a mean average precision (mAP) of 93.8%, showing improvements of 2.3%, 4.0%, 3.1%, and 2.7% respectively compared to the baseline model. These results indicate that the proposed detection method possesses significant detection performance and generalization capabilities.

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A tomato disease identification method based on leaf image automatic labeling algorithm and improved YOLOv5 model

Cited by 7 publications

References 40 publications

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

Vegetable disease detection using an improved YOLOv8 algorithm in the greenhouse plant environment

A ResNet50-DPA model for tomato leaf disease identification

Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion

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