Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model

Kumar, V. Senthil; Jaganathan, M.; Viswanathan, A.; Umamaheswari, M.; Vignesh, J.

doi:10.1088/2515-7620/acdece

Cited by 17 publications

(6 citation statements)

References 40 publications

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“…(2022) ; Jia et al. (2023) ; Kumar et al. (2023) , and Prasomphan (2023) did not fully consider the complexities of field conditions and the diverse angles at which diseases appear under the UAV viewpoint.…”

Section: Discussionmentioning

confidence: 99%

“…This fusion yielded impressive results, with a detection accuracy of 97.53% and a processing time of 0.62 s. In a similar vein, Prasomphan ( 2023) utilized the YOLOv3 model, achieving a notable AP of 89.6%. Kumar et al (2023) introduced a multi-scale YOLOv5 detection network. This innovation in detection accuracy is achieved through the integration of the DAIS segmentation and Bi-FAPN networks.…”

Section: Introductionmentioning

confidence: 99%

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Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles

Pan,

Guo,

Zheng

et al. 2023

Front. Plant Sci.

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Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles

Pan,

Guo,

Zheng

et al. 2023

Front. Plant Sci.

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“…To survive, they would dig a variety of shelter-filled burrows all around the nest. In ARA [ 41 ], a rabbit constantly builds D burrows throughout the search space’s dimensions and then chooses one at random to hide in to reduce the likelihood of being caught. The mathematical model of this behavior is illustrated in Equations (7)–(11):

…”

Section: Proposed As-hpoara Methodsmentioning

confidence: 99%

An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification

Khan,

Sangari,

Shukla

et al. 2023

Biomimetics

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In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%.

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“…Liu J et al [14] optimize the feature layer of the YOLOv3 model by using an image pyramid to achieve multi-scale feature detection, thereby improving the detection accuracy and speed of the Yolov3 model. V Senthil Kumar et al [15] present a multi-scale YOLOv5 detection network for the early detection and classification of rice crop diseases. The proposed Bidirectional Feature Attention Pyramid Network (Bi-FAPN) is used to extract the features from the segmented image and enhance the detection accuracy for diseases with different scales.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model

Wang,

Yi,

Huang

et al. 2024

Applied Sciences

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Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy.

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Rice leaf disease detection based on bidirectional feature attention pyramid network with YOLO v5 model

Cited by 17 publications

References 40 publications

Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles

Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles

An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification

Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model

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