Citrus Diseases and Pests Detection Model Based on Self-Attention YOLOV8

Luo, Dehuan; Xue, Yueju; Deng, Xinru; Yang, Bin; Chen, Haifei; Mo, Zhujiang

doi:10.1109/access.2023.3340148

Cited by 12 publications

(2 citation statements)

References 20 publications

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“…A variation of YOLOv8 was trained on RGB imagery of citrus leaves and fruits to detect and classify six different surface defects, including melanose, scab, and canker. Segmented defects were classified with 93% accuracy and 93% mAP [67].…”

Section: Deep Learning/yolomentioning

confidence: 99%

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

Frederick,

Burks,

Yadav

et al. 2024

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

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Citrus greening disease (HLB) and citrus canker are diseases afflicting Florida citrus groves, causing financial losses through smaller fruits, blemishes, premature fruit drop and/or eventual tree death. Often, symptoms of these resemble those of other defects/infections. Early detection of HLB and canker via in-grove leaf inspection can permit more effective mitigation tactics and more intelligent management of groves. Autonomous, vision-based disease scouting in a grove offers a financial benefit to the Florida citrus industry. This study investigates the potential of hyperspectral reflectance imagery (HSI) for detecting and classifying these conditions in the presence of other, less consequential leaf defects. Both sides of leaves with visible symptoms of HLB, canker, zinc deficiency, scab, melanose, greasy spot, and a control set were collected and imaged with a line-scan hyperspectral camera. Spectral bands from this imagery were selected using two methods: an unsupervised method based on principal component analysis (PCA), and a supervised method based on linear discriminant analysis (LDA). Using the selected bands, the YOLOv8 network architecture was trained to classify each side of these leaves. LDA-selected bands from the back of the leaves yielded an overall classification accuracy of 84.23%. Leaves with HLB and zinc deficiency were classified most accurately, with F1 scores of 0.977 and 0.953, respectively. On the back side of the leaf, recall of melanose was significantly improved by using the LDA bands. These findings favor the use of supervised band selection for HSI-based in-grove disease detection.

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Section: Deep Learning/yolomentioning

confidence: 99%

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

Frederick,

Burks,

Yadav

et al. 2024

Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IX

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“…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. D. Luo et al [16] put forward the Lightweight Self-Attention YOLOv8 model. They introduce an innovative feature fusion technique known as the asymptotic characteristic pyramid network (AFPN) at the Neck, thereby exhibiting an average increase in detection precision of 2.8% compared to YOLOv8.…”

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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Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

Frederick,

Burks,

Yadav

et al. 2024

Smart Agricultural Technology

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Citrus Diseases and Pests Detection Model Based on Self-Attention YOLOV8

Cited by 12 publications

References 20 publications

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

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

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

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