Crop disease detection based on enhanced YOLOv8

Jianghong Zhao; Jifu Zhao

doi:10.1117/12.3045482

20 September 2024 Crop disease detection based on enhanced YOLOv8

Jianghong Zhao, Jifu Zhao

Proceedings Volume 13269, Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024); 1326912 (2024) https://doi.org/10.1117/12.3045482
Event: Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Plant diseases have a great impact on global agricultural production and ecological security, which can cause the reduction of agricultural production and quality, and also bring food security problems. Traditional methods for detecting crop diseases rely heavily on manual observation and judgment, which suffer from issues such as long processing times, low efficiency, and dependence on expertise. In contrast, deep learning-based detection methods automate the identification and detection of crop disease images through training neural network models, offering advantages such as rapid processing speed, high accuracy, and enhanced efficiency. Therefore, this study improves the yolov8 network on the basis of deep learning to establish an accurate, stable and fast network detection model to provide a fast and efficient disease diagnosis solution for disease detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianghong Zhao and Jifu Zhao "Crop disease detection based on enhanced YOLOv8", Proc. SPIE 13269, Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), 1326912 (20 September 2024); https://doi.org/10.1117/12.3045482

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KEYWORDS

Object detection

Diseases and disorders

Target detection

Neurological disorders

Small targets

Agriculture

Convolution

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