Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning

Yin, Xia; Li, Wenhua; Li, Zhen; Yi, Lili

doi:10.25165/j.ijabe.20221503.7062

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…The model proposed in this paper performed cross-sectional performance comparison experiments with lightweight detection networks that were currently widely used in multiple fields. The selected comparison networks included the lightweight one-stage detection model YOLOX [ 91 ], the two-stage lightweight detection network ThunderNet [ 92 ], the ultra-lightweight Anchor Free detection network NanoDet [ 93 ], and detection networks using the lightweight backbone networks, such as ShuffleNetV2 [ 94 ] and MobileNetV3 [ 95 ], combined with the detectors proposed in this paper. The comparison experiments were subdivided into the detection of targets when shooting horizontally versus vertically, and the results of the comparison tests were shown in Figure 17 , Figure 18 , and Figure 19 , respectively.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Liu

et al. 2023

Sensors

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Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Liu

et al. 2023

Sensors

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“…Sethy et al (2020) successfully identified four rice leaf diseases by combining deep convolutional neural networks and SVM, demonstrating that integrating deep features with SVM can achieve excellent classification, achieving an F1 score of 0.9838 [24]. Yin et al (2022) successfully developed a grape leaf disease identification method using an improved MobileNetV3 model and deep transfer learning. This method achieved recognition accuracy of up to 99.84% with limited computational resources and dataset size, and the model size was only 30 MB [25].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8

Ding,

Jeon,

Rhee

et al. 2024

Preprint

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In light of the prevalent pest and disease issues faced by greenhouse cucumbers, a staple vegetable during winter, this study introduces a detection method based on the enhanced YOLOv8s model. This method aims to provide technical support for detecting and classifying pests and diseases in cucumber agricultural production. The model integrates the 'MultiCat' module for multiscale feature fusion and employs the 'C2fe' and 'ADC2f'modules to strengthen spatial and channel attention. The 'Block2d' function also facilitates the choice between average pooling and attention-based spatial pooling. Channel fusion is achieved through additive and multiplicative operations, allowing the model to delve deeper into feature learning. Experimental results confirm that our approach outperforms the original YOLOv8s model in pest detection, particularly excelling in the identification of small-scale and overlapping afflictions.

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“…This study utilizes the enhanced YOLOv8 object detection network as the base model, incorporating an improved RFCBAM in place of commonly used lightweight feature extraction backbones like MobileNetV3 [37], MobileNetV2 [38], GhostNetV2 [39], and Shuf-fleNetV2 [40]. By maintaining consistent parameters except for the backbone network, the experimental results in Table 2 demonstrate the varying training effects of different backbone networks.…”

Section: Comparative Experiments 341 Backbone Network Comparison Expe...mentioning

confidence: 99%

YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea

Ye,

Shao,

et al. 2024

Sensors

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In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of similar phenotypic symptoms. This method focuses on detecting tea leaf blight, tea white spot, tea sooty leaf disease, and tea ring spot as the research objects. This paper presents an enhancement to the YOLOv8 network framework by introducing the Receptive Field Concentration-Based Attention Module (RFCBAM) into the backbone network to replace C2f, thereby improving feature extraction capabilities. Additionally, a mixed pooling module (Mixed Pooling SPPF, MixSPPF) is proposed to enhance information blending between features at different levels. In the neck network, the RepGFPN module replaces the C2f module to further enhance feature extraction. The Dynamic Head module is embedded in the detection head part, applying multiple attention mechanisms to improve multi-scale spatial location and multi-task perception capabilities. The inner-IoU loss function is used to replace the original CIoU, improving learning ability for small lesion samples. Furthermore, the AKConv block replaces the traditional convolution Conv block to allow for the arbitrary sampling of targets of various sizes, reducing model parameters and enhancing disease detection. the experimental results using a self-built dataset demonstrate that the enhanced YOLOv8-RMDA exhibits superior detection capabilities in detecting small target disease areas, achieving an average accuracy of 93.04% in identifying early tea lesions. When compared to Faster R-CNN, MobileNetV2, and SSD, the average precision rates of YOLOv5, YOLOv7, and YOLOv8 have shown improvements of 20.41%, 17.92%, 12.18%, 12.18%, 10.85%, 7.32%, and 5.97%, respectively. Additionally, the recall rate (R) has increased by 15.25% compared to the lowest-performing Faster R-CNN model and by 8.15% compared to the top-performing YOLOv8 model. With an FPS of 132, YOLOv8-RMDA meets the requirements for real-time detection, enabling the swift and accurate identification of early tea diseases. This advancement presents a valuable approach for enhancing the ecological tea industry in Yunnan, ensuring its healthy development.

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Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning

Cited by 16 publications

References 24 publications

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8

YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea

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