YOLO Network-Based for Detection of Rice Leaf Disease

Aziz, Faruq; Ernawan, Ferda; Fakhreldin, Mohammad; Adi, Prajanto Wahyu

doi:10.1109/icitri59340.2023.10249843

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Sun et al [18] incorporated phantom convolution and attention modules into YOLOv5s for the recognition of apple fruit diseases. Aziz et al [19] proposed an improved YOLO to classify diseased rice leaves with 94% accuracy. Sangaiah et al [20] proposed a T-yolo-Rice rice disease detection model, and the mAP reached 86%.…”

Section: Introductionmentioning

confidence: 99%

Rice Diseases Identification Method Based on Improved YOLOv7-Tiny

Cheng,

Zhao,

Feng

2024

Agriculture

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The accurate and rapid identification of rice diseases is crucial for enhancing rice yields. However, this task encounters several challenges: (1) Complex background problem: The rice background in a natural environment is complex, which interferes with rice disease recognition; (2) Disease region irregularity problem: Some rice diseases exhibit irregular shapes, and their target regions are small, making them difficult to detect; (3) Classification and localization problem: Rice disease recognition employs identical features for both classification and localization tasks, thereby affecting the training effect. To address the aforementioned problems, an enhanced rice disease recognition model leveraging the improved YOLOv7-Tiny is proposed. Specifically, in order to reduce the interference of complex background, the YOLOv7-Tiny model’s backbone network has been enhanced by incorporating the Convolutional Block Attention Module (CBAM); subsequently, to address the irregularity issue in the disease region, the RepGhost bottleneck module, which is based on structural reparameterization techniques, has been introduced; Finally, to resolve the classification and localization issue, a lightweight YOLOX decoupled head has been proposed. The experimental results have demonstrated that: (1) The enhanced YOLOv7-Tiny model demonstrated elevated F1 scores and mAP@.5, achieving 0.894 and 0.922, respectively, on the rice pest and disease dataset. These scores exceeded the original YOLOv7-Tiny model’s performance by margins of 3.1 and 2.2 percentage points, respectively. (2) In comparison to the YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5-S, YOLOX-S, and YOLOv7-Tiny models, the enhanced YOLOv7-Tiny model achieved higher F1 scores and mAP@.5. The improved YOLOv7-Tiny model boasts a single image inference time of 26.4 ms, satisfying the requirement for real-time identification of rice diseases and facilitating deployment in embedded devices.

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Section: Introductionmentioning

confidence: 99%

Rice Diseases Identification Method Based on Improved YOLOv7-Tiny

Cheng,

Zhao,

Feng

2024

Agriculture

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“…The You Only Look Once version 9 (YOLOv9) is a state-of-the-art deep learning model renowned for its real-time object detection capabilities [6], [7], [8], [9]. Originally developed for general object detection tasks, YOLOv9 has shown promising potential for medical imaging applications, including skin lesion detection.…”

Section: Introductionmentioning

confidence: 99%

Efficient Skin Lesion Detection using YOLOv9 Network

Faruq Aziz,

Saputri

2024

MEDINFTech

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Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection

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