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2025 | OriginalPaper | Buchkapitel

Wheat Disease Detection Using YOLOv8 and GAN Model

verfasst von : Dayal Rohan Volety, RamanThakur, Sushruta Mishra, Shalini Goel, Rachit Garg, Nagendar Yamsani

Erschienen in: Innovative Computing and Communications

Verlag: Springer Nature Singapore

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Abstract

Wheat leaf disease is a major concern in agriculture. It leads to significant crop yield losses. It is necessary to diagnose wheat leaf disease in its early stages to ensure food security and sustain global wheat production. The main objective of this paper is to present a different method for wheat disease detection using you only look once algorithm version 8 (YOLOv8) and generative adversarial networks (GANs). YOLOv8 is a famous object detection method which can detect and classify objects in real time accurately. It can process the images very quickly and accurately, thus making it an ideal choice for this task. One major problem is limited training data for various wheat diseases. To address this problem, in the proposed research, the authors have introduced a conditional-generative adversarial network (C-GAN)-based data augmentation technique which generates synthetic images of wheat leaves. This technique increases the volume of dataset to be used in training, thus improving the overall generalization of the model. The proposed model further compares the YOLOv8-trained model with other existing models. The proposed model achieves an accuracy of 99.8%, which is better than other models.

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Literatur
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Zurück zum Zitat Yang, G., Wang, J., Nie, Z., Yang, H., & Yu, S. (1824). A lightweight YOLOv8 tomato detection algorithm combining feature enhancement and attention. Agronomy, 2023, 13. Yang, G., Wang, J., Nie, Z., Yang, H., & Yu, S. (1824). A lightweight YOLOv8 tomato detection algorithm combining feature enhancement and attention. Agronomy, 2023, 13.
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Zurück zum Zitat Wang, G., Chen, Y., An, P., Hong, H., Hu, J., & Huang, T. (2023). UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios. Sensors (Basel, Switzerland), 23. Wang, G., Chen, Y., An, P., Hong, H., Hu, J., & Huang, T. (2023). UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios. Sensors (Basel, Switzerland), 23.
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Zurück zum Zitat Srivastava, A., Samanta, S., Mishra, S., Alkhayyat, A., Gupta, D., & Sharma, V. (2023). Medi-assist: A decision tree based chronic diseases detection model. In 2023 4th International Conference on Intelligent Engineering and Management (ICIEM) (pp. 1–7). London, United Kingdom. https://doi.org/10.1109/ICIEM59379.2023.10167400 Srivastava, A., Samanta, S., Mishra, S., Alkhayyat, A., Gupta, D., & Sharma, V. (2023). Medi-assist: A decision tree based chronic diseases detection model. In 2023 4th International Conference on Intelligent Engineering and Management (ICIEM) (pp. 1–7). London, United Kingdom. https://​doi.​org/​10.​1109/​ICIEM59379.​2023.​10167400
Metadaten
Titel
Wheat Disease Detection Using YOLOv8 and GAN Model
verfasst von
Dayal Rohan Volety
RamanThakur
Sushruta Mishra
Shalini Goel
Rachit Garg
Nagendar Yamsani
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-4152-6_25